research topics of data science

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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• Identifying fake news using real-time analytics:  The circulation of fake news has become a pressing issue in the modern era. The data gathered from social media networks might seem legit, but sometimes they are not. The sources that provide the data are unauthenticated most of the time, which makes it a crucial issue to be addressed.
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• Secure federated learning with real-world applications : Federated learning is a technique that trains an algorithm across multiple decentralized edge devices and servers. This technique can be adopted to build models locally, but if this technique can be deployed at scale or not, across multiple platforms with high-level security is still obscure.
• Big data analytics and its impact on marketing strategy : The advent of data science and big data analytics has entirely redefined the marketing industry. It has helped enterprises by offering valuable insights into their existing and future customers. But several issues like the existence of surplus data, integrating complex data into customers’ journeys, and complete data privacy are some of the branches that are still untrodden and need immediate attention.
• Impact of big data on business decision-making: Present studies signify that big data has transformed the way managers and business leaders make critical decisions concerning the growth and development of the business. It allows them to access objective data and analyse the market environments, enabling companies to adapt rapidly and make decisions faster. Working on this topic will help students understand the present market and business conditions and help them analyse new solutions.
• Implementing big data to understand consumer behaviour : In understanding consumer behaviour, big data is used to analyse the data points depicting a consumer’s journey after buying a product. Data gives a clearer picture in understanding specific scenarios. This topic will help understand the problems that businesses face in utilizing the insights and develop new strategies in the future to generate more ROI.
• Applications of big data to predict future demand and forecasting : Predictive analytics in data science has emerged as an integral part of decision-making and demand forecasting. Working on this topic will enable the students to determine the significance of the high-quality historical data analysis and the factors that drive higher demand in consumers.
• The importance of data exploration over data analysis : Exploration enables a deeper understanding of the dataset, making it easier to navigate and use the data later. Intelligent analysts must understand and explore the differences between data exploration and analysis and use them according to specific needs to fulfill organizational requirements.
• Data science and software engineering : Software engineering and development are a major part of data science. Skilled data professionals should learn and explore the possibilities of the various technical and software skills for performing critical AI and big data tasks.

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99+ Interesting Data Science Research Topics For Students In 2024

In today’s information-driven world, data science research stands as a pivotal domain shaping our understanding and application of vast data sets. It amalgamates statistics, computer science, and domain knowledge to extract valuable insights from data. Understanding ‘What Is Data Science?’ is fundamental—a field exploring patterns, trends, and solutions embedded within data.

However, the significance of data science research papers in a student’s life cannot be overstated. They foster critical thinking, analytical skills, and a deeper comprehension of the subject matter. To aid students in navigating this realm effectively, this blog dives into essential elements integral to a data science research paper, while also offering a goldmine of 99+ engaging and timely data science research topics for 2024.

Unraveling tips for crafting an impactful research paper and insights on choosing the right topic, this blog is a compass for students exploring data science research topics. Stay tuned to unearth more about ‘data science research topics’ and refine your academic journey.

What Is Data Science?

Table of Contents

Data Science is like a detective for information! It’s all about uncovering secrets and finding valuable stuff in heaps of data. Imagine you have a giant puzzle with tons of pieces scattered around. Data Science helps in sorting these pieces and figuring out the picture they create. It uses tools and skills from math, computer science, and knowledge about different fields to solve real-world problems.

In simpler terms, Data Science is like a chef in a kitchen, blending ingredients to create a perfect dish. Instead of food, it combines data—numbers, words, pictures—to cook up solutions. It helps in understanding patterns, making predictions, and answering tricky questions by exploring data from various sources. In essence, Data Science is the magic that turns data chaos into meaningful insights that can guide decisions and make life better.

Importance Of Data Science Research Paper In Student’s Life

Data Science research papers are like treasure maps for students! They’re super important because they teach students how to explore and understand the world of data. Writing these papers helps students develop problem-solving skills, think critically, and become better at analyzing information. It’s like a fun adventure where they learn how to dig into data and uncover valuable insights that can solve real-world problems.

• Enhances critical thinking: Research papers challenge students to analyze and interpret data critically, honing their thinking skills.
• Fosters analytical abilities: Students learn to sift through vast amounts of data, extracting meaningful patterns and information.
• Encourages exploration: Engaging in research encourages students to explore diverse data sources, broadening their knowledge horizon.
• Develops communication skills: Writing research papers hones students’ ability to articulate complex findings and ideas clearly.
• Prepares for real-world challenges: Through research, students learn to apply theoretical knowledge to practical problems, preparing them for future endeavors.

Elements That Must Be Present In Data Science Research Paper

Here are some elements that must be present in data science research paper:

1. Clear Objective

A data science research paper should start with a clear goal, stating what the study aims to investigate or achieve. This objective guides the entire paper, helping readers understand the purpose and direction of the research.

2. Detailed Methodology

Explaining how the research was conducted is crucial. The paper should outline the tools, techniques, and steps used to collect, analyze, and interpret data. This section allows others to replicate the study and validate its findings.

3. Accurate Data Presentation

Presenting data in an organized and understandable manner is key. Graphs, charts, and tables should be used to illustrate findings clearly, aiding readers’ comprehension of the results.

4. Thorough Analysis and Interpretation

Simply presenting data isn’t enough; the paper should delve into a deep analysis, explaining the meaning behind the numbers. Interpretation helps draw conclusions and insights from the data.

5. Conclusive Findings and Recommendations

A strong conclusion summarizes the key findings of the research. It should also offer suggestions or recommendations based on the study’s outcomes, indicating potential avenues for future exploration.

Here are some interesting data science research topics for students in 2024:

Natural Language Processing (NLP)

• Multi-modal Contextual Understanding: Integrating text, images, and audio to enhance NLP models’ comprehension abilities.
• Cross-lingual Transfer Learning: Investigating methods to transfer knowledge from one language to another for improved translation and understanding.
• Emotion Detection in Text: Developing models to accurately detect and interpret emotions conveyed in textual content.
• Sarcasm Detection in Social Media: Building algorithms that can identify and understand sarcastic remarks in online conversations.
• Language Generation for Code: Generating code snippets and scripts from natural language descriptions using NLP techniques.
• Bias Mitigation in Language Models: Developing strategies to mitigate biases present in large language models and ensure fairness in generated content.
• Dialogue Systems for Personalized Assistance: Creating intelligent conversational agents that provide personalized assistance based on user preferences and history.
• Summarization of Legal Documents: Developing NLP models capable of summarizing lengthy legal documents for quick understanding and analysis.
• Understanding Contextual Nuances in Sentiment Analysis: Enhancing sentiment analysis models to better comprehend contextual nuances and sarcasm in text.
• Hate Speech Detection and Moderation: Building systems to detect and moderate hate speech and offensive language in online content.

Computer Vision

• Weakly Supervised Object Detection: Exploring methods to train object detection models with limited annotated data.
• Video Action Recognition in Uncontrolled Environments: Developing models that can recognize human actions in videos captured in uncontrolled settings.
• Image Generation and Translation: Investigating techniques to generate realistic images from textual descriptions and translate images across different domains.
• Scene Understanding in Autonomous Systems: Enhancing computer vision algorithms for better scene understanding in autonomous vehicles and robotics.
• Fine-grained Visual Classification: Improving models to classify objects at a more granular level, distinguishing subtle differences within similar categories.
• Visual Question Answering (VQA): Creating systems capable of answering questions based on visual input, requiring reasoning and comprehension abilities.
• Medical Image Analysis for Disease Diagnosis: Developing computer vision models for accurate and early diagnosis of diseases from medical images.
• Action Localization in Videos: Building models to precisely localize and recognize specific actions within video sequences.
• Image Captioning with Contextual Understanding: Generating captions for images considering the context and relationships between objects.
• Human Pose Estimation in Real-time: Improving algorithms for real-time estimation of human poses in videos for applications like motion analysis and gaming.

Machine Learning Algorithms

• Self-supervised Learning Techniques: Exploring novel methods for training machine learning models without explicit supervision.
• Continual Learning in Dynamic Environments: Investigating algorithms that can continuously learn and adapt to changing data distributions and tasks.
• Explainable AI for Model Interpretability: Developing techniques to explain the decisions and predictions made by complex machine learning models.
• Transfer Learning for Small Datasets: Techniques to effectively transfer knowledge from large datasets to small or domain-specific datasets.
• Adaptive Learning Rate Optimization: Enhancing optimization algorithms to dynamically adjust learning rates based on data characteristics.
• Robustness to Adversarial Attacks: Building models resistant to adversarial attacks, ensuring stability and security in machine learning applications.
• Active Learning Strategies: Investigating methods to select and label the most informative data points for model training to minimize labeling efforts.
• Privacy-preserving Machine Learning: Developing algorithms that can train models on sensitive data while preserving individual privacy.
• Fairness-aware Machine Learning: Techniques to ensure fairness and mitigate biases in machine learning models across different demographics.
• Multi-task Learning for Jointly Learning Tasks: Exploring approaches to jointly train models on multiple related tasks to improve overall performance.

Deep Learning

• Graph Neural Networks for Representation Learning: Using deep learning techniques to learn representations from graph-structured data.
• Transformer Models for Image Processing: Adapting transformer architectures for image-related tasks, such as image classification and generation.
• Few-shot Learning Strategies: Investigating methods to enable deep learning models to learn from a few examples in new categories.
• Memory-Augmented Neural Networks: Enhancing neural networks with external memory for improved learning and reasoning capabilities.
• Neural Architecture Search (NAS): Automating the design of neural network architectures for specific tasks or constraints.
• Meta-learning for Fast Adaptation: Developing models capable of quickly adapting to new tasks or domains with minimal data.
• Deep Reinforcement Learning for Robotics: Utilizing deep RL techniques for training robots to perform complex tasks in real-world environments.
• Generative Adversarial Networks (GANs) for Data Augmentation: Using GANs to generate synthetic data for enhancing training datasets.
• Variational Autoencoders for Unsupervised Learning: Exploring VAEs for learning latent representations of data without explicit supervision.
• Lifelong Learning in Deep Networks: Strategies to enable deep networks to continually learn from new data while retaining past knowledge.

Big Data Analytics

• Streaming Data Analysis for Real-time Insights: Techniques to analyze and derive insights from continuous streams of data in real-time.
• Scalable Algorithms for Massive Graphs: Developing algorithms that can efficiently process and analyze large-scale graph-structured data.
• Anomaly Detection in High-dimensional Data: Detecting anomalies and outliers in high-dimensional datasets using advanced statistical methods and machine learning.
• Personalization and Recommendation Systems: Enhancing recommendation algorithms for providing personalized and relevant suggestions to users.
• Data Quality Assessment and Improvement: Methods to assess, clean, and enhance the quality of big data to improve analysis and decision-making.
• Time-to-Event Prediction in Time-series Data: Predicting future events or occurrences based on historical time-series data.
• Geospatial Data Analysis and Visualization: Analyzing and visualizing large-scale geospatial data for various applications such as urban planning, disaster management, etc.
• Privacy-preserving Big Data Analytics: Ensuring data privacy while performing analytics on large-scale datasets in distributed environments.
• Graph-based Deep Learning for Network Analysis: Leveraging deep learning techniques for network analysis and community detection in large-scale networks.
• Dynamic Data Compression Techniques: Developing methods to compress and store large volumes of data efficiently without losing critical information.

Healthcare Analytics

• Predictive Modeling for Patient Outcomes: Using machine learning to predict patient outcomes and personalize treatments based on individual health data.
• Clinical Natural Language Processing for Electronic Health Records (EHR): Extracting valuable information from unstructured EHR data to improve healthcare delivery.
• Wearable Devices and Health Monitoring: Analyzing data from wearable devices to monitor and predict health conditions in real-time.
• Drug Discovery and Development using AI: Utilizing machine learning and AI for efficient drug discovery and development processes.
• Predictive Maintenance in Healthcare Equipment: Developing models to predict and prevent equipment failures in healthcare settings.
• Disease Clustering and Stratification: Grouping diseases based on similarities in symptoms, genetic markers, and response to treatments.
• Telemedicine Analytics: Analyzing data from telemedicine platforms to improve remote healthcare delivery and patient outcomes.
• AI-driven Radiomics for Medical Imaging: Using AI to extract quantitative features from medical images for improved diagnosis and treatment planning.
• Healthcare Resource Optimization: Optimizing resource allocation in healthcare facilities using predictive analytics and operational research techniques.
• Patient Journey Analysis and Personalized Care Pathways: Analyzing patient trajectories to create personalized care pathways and improve healthcare outcomes.

Time Series Analysis

• Forecasting Volatility in Financial Markets: Predicting and modeling volatility in stock prices and financial markets using time series analysis.
• Dynamic Time Warping for Similarity Analysis: Utilizing DTW to measure similarities between time series data, especially in scenarios with temporal distortions.
• Seasonal Pattern Detection and Analysis: Identifying and modeling seasonal patterns in time series data for better forecasting.
• Time Series Anomaly Detection in Industrial IoT: Detecting anomalies in industrial sensor data streams to prevent equipment failures and improve maintenance.
• Multivariate Time Series Forecasting: Developing models to forecast multiple related time series simultaneously, considering interdependencies.
• Non-linear Time Series Analysis Techniques: Exploring non-linear models and methods for analyzing complex time series data.
• Time Series Data Compression for Efficient Storage: Techniques to compress and store time series data efficiently without losing crucial information.
• Event Detection and Classification in Time Series: Identifying and categorizing specific events or patterns within time series data.
• Time Series Forecasting with Uncertainty Estimation: Incorporating uncertainty estimation into time series forecasting models for better decision-making.
• Dynamic Time Series Graphs for Network Analysis: Representing and analyzing dynamic relationships between entities over time using time series graphs.

Reinforcement Learning

• Multi-agent Reinforcement Learning for Collaboration: Developing strategies for multiple agents to collaborate and solve complex tasks together.
• Hierarchical Reinforcement Learning: Utilizing hierarchical structures in RL for solving tasks with varying levels of abstraction and complexity.
• Model-based Reinforcement Learning for Sample Efficiency: Incorporating learned models into RL for efficient exploration and planning.
• Robotic Manipulation with Reinforcement Learning: Training robots to perform dexterous manipulation tasks using RL algorithms.
• Safe Reinforcement Learning: Ensuring that RL agents operate safely and ethically in real-world environments, minimizing risks.
• Transfer Learning in Reinforcement Learning: Transferring knowledge from previously learned tasks to expedite learning in new but related tasks.
• Curriculum Learning Strategies in RL: Designing learning curricula to gradually expose RL agents to increasingly complex tasks.
• Continuous Control in Reinforcement Learning: Exploring techniques for continuous control tasks that require precise actions in a continuous action space.
• Reinforcement Learning for Adaptive Personalization: Utilizing RL to personalize experiences or recommendations for individuals in dynamic environments.
• Reinforcement Learning in Healthcare Decision-making: Using RL to optimize treatment strategies and decision-making in healthcare settings.

Data Mining

• Graph Mining for Social Network Analysis: Extracting valuable insights from social network data using graph mining techniques.
• Sequential Pattern Mining for Market Basket Analysis: Discovering sequential patterns in customer purchase behaviors for market basket analysis.
• Clustering Algorithms for High-dimensional Data: Developing clustering techniques suitable for high-dimensional datasets.
• Frequent Pattern Mining in Healthcare Datasets: Identifying frequent patterns in healthcare data for actionable insights and decision support.
• Outlier Detection and Fraud Analysis: Detecting anomalies and fraudulent activities in various domains using data mining approaches.
• Opinion Mining and Sentiment Analysis in Reviews: Analyzing opinions and sentiments expressed in product or service reviews to derive insights.
• Data Mining for Personalized Learning: Mining educational data to personalize learning experiences and adapt teaching methods.
• Association Rule Mining in Internet of Things (IoT) Data: Discovering meaningful associations and patterns in IoT-generated data streams.
• Multi-modal Data Fusion for Comprehensive Analysis: Integrating information from multiple data modalities for a holistic understanding and analysis.
• Data Mining for Energy Consumption Patterns: Analyzing energy usage data to identify patterns and optimize energy consumption in various sectors.

Ethical AI and Bias Mitigation

• Fairness Metrics and Evaluation in AI Systems: Developing metrics and evaluation frameworks to assess the fairness of AI models.
• Bias Detection and Mitigation in Facial Recognition: Addressing biases present in facial recognition systems to ensure equitable performance across demographics.
• Algorithmic Transparency and Explainability: Designing methods to make AI algorithms more transparent and understandable to stakeholders.
• Fair Representation Learning in Unbalanced Datasets: Learning fair representations from imbalanced data to reduce biases in downstream tasks.
• Fairness-aware Recommender Systems: Ensuring fairness and reducing biases in recommendation algorithms across diverse user groups.
• Ethical Considerations in AI for Criminal Justice: Investigating the ethical implications of AI-based decision-making in criminal justice systems.
• Debiasing Techniques in Natural Language Processing: Developing methods to mitigate biases in language models and text generation.
• Diversity and Fairness in Hiring Algorithms: Ensuring diversity and fairness in AI-based hiring systems to minimize biases in candidate selection.
• Ethical AI Governance and Policy: Examining the role of governance and policy frameworks in regulating the development and deployment of AI systems.
• AI Accountability and Responsibility: Addressing ethical dilemmas and defining responsibilities concerning AI system behaviors and decision-making processes.

Tips For Writing An Effective Data Science Research Paper

Here are some tips for writing an effective data science research paper:

Tip 1: Thorough Planning and Organization

Begin by planning your research paper carefully. Outline the sections and information you’ll include, ensuring a logical flow from introduction to conclusion. This organized approach makes writing easier and helps maintain coherence in your paper.

Tip 2: Clarity in Writing Style

Use clear and simple language to communicate your ideas. Avoid jargon or complex terms that might confuse readers. Write in a way that is easy to understand, ensuring your message is effectively conveyed.

Tip 3: Precise and Relevant Information

Include only information directly related to your research topic. Ensure the data, explanations, and examples you use are precise and contribute directly to supporting your arguments or findings.

Tip 4: Effective Data Visualization

Utilize graphs, charts, and tables to present your data visually. Visual aids make complex information easier to comprehend and can enhance the overall presentation of your research findings.

Tip 5: Review and Revise

Before submitting your paper, review it thoroughly. Check for any errors in grammar, spelling, or formatting. Revise sections if necessary to ensure clarity and coherence in your writing. Asking someone else to review it can also provide valuable feedback.

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Things To Remember While Choosing The Data Science Research Topic

When selecting a data science research topic, consider your interests and its relevance to the field. Ensure the topic is neither too broad nor too narrow, striking a balance that allows for in-depth exploration while staying manageable.

• Relevance and Significance: Choose a topic that aligns with current trends or addresses a significant issue in the field of data science.
• Feasibility : Ensure the topic is researchable within the resources and time available. It should be practical and manageable for the scope of your study.
• Your Interest and Passion: Select a topic that genuinely interests you. Your enthusiasm will drive your motivation and engagement throughout the research process.
• Availability of Data: Check if there’s sufficient data available for analysis related to your chosen topic. Accessible and reliable data sources are vital for thorough research.
• Potential Contribution: Consider how your chosen topic can contribute to existing knowledge or fill a gap in the field. Aim for a topic that adds value and insights to the data science domain.

In wrapping up our exploration of data science research topics, we’ve uncovered a world of importance and guidance for students. From defining data science to understanding its impact on student life, identifying essential elements in research papers, offering a multitude of intriguing topics for 2024, to providing tips for crafting effective papers—the journey has been insightful. 

Remembering the significance of topic selection and the key components of a well-structured paper, this voyage emphasizes how data science opens doors to endless opportunities. It’s not just a subject; it’s the compass guiding tomorrow’s discoveries and innovations in our digital landscape.

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Our work supports research in a variety of fields where incredible advances are being made through the facilitation of meaningful collaborations between domain researchers, with deep expertise in societal and fundamental research challenges, and methods researchers that are developing next-generation computational tools and techniques, including:

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In recent years, wildfire has gone from an infrequent and distant news item to a centerstage isssue spanning many consecutive weeks for urban and suburban communities. Frequent wildfires are changing everyday lives for California in numerous ways -- from public safety power shutoffs to hazardous air quality -- that seemed inconceivable as recently as 2015. Moreover, elevated wildfire risk in the western United States (and similar climates globally) is here to stay into the foreseeable future. There is a plethora of problems that need solutions in the wildland fire arena; many of them are well suited to a data-driven approach.

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Many of the most pressing questions in empirical economics concern causal questions, such as the impact, both short and long run, of educational choices on labor market outcomes, and of economic policies on distributions of outcomes. This makes them conceptually quite different from the predictive type of questions that many of the recently developed methods in machine learning are primarily designed for.

Data Science for Education

Educational data spans K-12 school and district records, digital archives of instructional materials and gradebooks, as well as student responses on course surveys. Data science of actual classroom interaction is also of increasing interest and reality.

Data Science for Human Health

It is clear that data science will be a driving force in transitioning the world’s healthcare systems from reactive “sick-based” care to proactive, preventive care.

Data Science for Humanity

Our modern era is characterized by massive amounts of data documenting the behaviors of individuals, groups, organizations, cultures, and indeed entire societies. This wealth of data on modern humanity is accompanied by massive digitization of historical data, both textual and numeric, in the form of historic newspapers, literary and linguistic corpora, economic data, censuses, and other government data, gathered and preserved over centuries, and newly digitized, acquired, and provisioned by libraries, scholars, and commercial entities.

Data Science for Linguistics

The impact of data science on linguistics has been profound. All areas of the field depend on having a rich picture of the true range of variation, within dialects, across dialects, and among different languages. The subfield of corpus linguistics is arguably as old as the field itself and, with the advent of computers, gave rise to many core techniques in data science.

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Many key sustainability issues translate into decision and optimization problems and could greatly benefit from data-driven decision making tools. In fact, the impact of modern information technology has been highly uneven, mainly benefiting large firms in profitable sectors, with little or no benefit in terms of the environment. Our vision is that data-driven methods can — and should — play a key role in increasing the efficiency and effectiveness of the way we manage and allocate our natural resources.

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The practice of data analysis has changed enormously. Data science needs to find new inferential paradigms that allow data exploration prior to the formulation of hypotheses.

7 Key Data Science Trends For 2024-2027

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Here are the 7 fastest-growing data science trends for 2024 and beyond.

We'll also outline how these trends will impact both data scientists’ work and everyday life.

Whether you’re actively involved in the data science community, or just concerned about your data privacy, these are the top trends to monitor.

1. Explosion in deepfake video and audio

Deepfakes use artificial intelligence to manipulate or create content to represent someone else.

Often this is an image or video of one person modified to someone else’s likeness.

But it can be audio too.

Back in 2019, an AI company deepfaked popular podcaster Joe Rogan’s voice so effectively it instantly went viral on social media.

And the tech has only improved since.

There’s huge scope for this technology to be used maliciously.

Another voice deep fake was used to scam a UK-based energy company out of €220,000 .

The CEO believed he was on the phone with a colleague and was told to urgently transfer the money to the bank account of a Hungarian supplier.

In fact, the call had been spoofed with deep fake technology to mimic the man’s voice and “melody”.

In fact, there's growing search interest in a practice known as "voice phishing". Which is essentially the "official" term for the practice.

As well as hoaxes and financial fraud, deepfakes can also be weaponized to discredit business figures and politicians.

Governments are starting to protect against this with legislation and social media regulation.

And with technology that can identify deepfake videos.

But the battle with deepfakes has only just begun.

2. More applications created with Python

Python is the go-to programming language for data analysis.

Why is this?

Because Python has a huge number of free data science libraries such as Pandas and machine learning libraries like Scikit-learn .

It can even be used to develop blockchain applications.

Add to this a friendly learning curve for beginners, and you have a recipe for success.

Python is now ranked as the 3rd most popular language in general by the analyst firm RedMonk.

And the popularity growth trend shows it’s on track to become number 1 within the next three years.

3. Increased demand for End-to-end AI solutions

“Dataiku” searches are up by 146% in 5 years, growing quickly even before Google acquired them.

Enterprise AI company Dataiku is now worth $4.6 billion ( according to TechCrunch ) after Google bought a stake in the company in December 2019.

The AI startup helps enterprise customers clean their large data sets and build machine learning models.

This way, companies like General Electric and Unilever can gain valuable, deep-learning insights from their massive amounts of data.

And automate important data management tasks.

Previously, businesses would have to seek expertise in all the different parts of the process and piece it together themselves.

But Dataiku handles the entire data science cycle from start to finish with a single product.

And because of this, they stand out.

Businesses want end-to-end data science solutions. And startups that provide this will eat the market.

4. Companies hire more data analysts

“Data analyst” searches are up by 265% in 5 years. Interest in this data science role displays hockey stick growth.

Demand for data analysts has shot through the roof over the last few years.

And, thanks largely to data coming in from the Internet of Things (IoT) and advances in cloud computing, global data storage is set to grow from 45 zettabytes to 175 zettabytes by 2025 .

So the need for experts to parse and analyze all of this data is set to rise.

Why are so many data analysts required?

After all, there are plenty of data analytics programs out there that can sort through it all.

And "digital transformation" has supposedly replaced many human-led business tasks.

Sure, machines can help analyze data.

But big data is often extremely messy and lacking in proper structure.

Which is why humans are needed to manually tidy training data before it is ingested by machine learning algorithms.

It’s also increasingly common for data people to be involved on the output end too.

AI-produced results are not always reliable or accurate, so machine learning companies often use humans to clean up the final data.

And write up an analysis of what they find in a way that non-tech stakeholders can understand it.

Amazon's Mechanical Turk is the biggest platform where "Turkers" complete data labeling and cleaning jobs .

The data science and machine learning methods of the 2020s will be less artificial and automated than initially expected.

Augmented intelligence and human-in-the-loop artificial intelligence will likely become a big trend in data science.

5. Data scientists joining Kaggle

Search growth for “Kaggle” has increased by 223% over 5 years. The data science platform has over 5 million users across 194 countries.

Kaggle has grown quickly to become the world's largest data science community.

And with over 8 million users across 194 countries, it’s not slowing down.

Many budding data scientists now start with Kaggle to begin their machine learning journey. 

And post the progress of their machine learning projects in real-time.

Users can even share data sets and enter competitions to solve data science challenges with neural networks.

Or work with other data scientists to build models in Kaggle’s web-based data science workbench.

Kaggle competitions can have hefty prize sums.

Academic papers have actually been published based on Kaggle competition findings too.

Successful projects from Kaggle’s hundreds of competitions will likely continue to push boundaries in the field of data science.

6. Increased interest in consumer data protection

“Data privacy”  has seen a search growth of 441% over the last 10 years. People are now searching about their data privacy in greater numbers by the month.

Consumer awareness about data privacy rose in the wake of the Cambridge Analytica scandal .

In fact, CIGI-Ipsos found that more than half of all consumers became more interested in data privacy in the year following the revelations.

Platforms like Facebook and Google, which previously harvested and shared user data freely, have since faced legal backlash and public scrutiny.

Facebook now has a large guide on privacy basics and what it does with your data.

This broader data privacy trend means that large data sets will soon be walled off and harder to come by.

Businesses and data scientists will need to navigate legislation such as the California Consumer Privacy Act which came into effect at the start of 2020.

And this could become a bane for data science when it comes to the future acquisition and use of consumer data.

7. AI devs combating adversarial machine learning

“Adversarial machine learning” searches have grown significantly in the last decade by 2,500%.

Adversarial machine learning is where an attacker inputs data into a machine learning model with the aim of causing mistakes.

Essentially, it is an optical illusion designed for a machine.

Adversarial Fashion's clothing lines trick machine-learning models with bold patterns and lettering.

Anti-surveillance clothing takes this approach to the masses.

They’re specifically designed to confuse face detection algorithms with bold shapes and patterns.

According to a Northeastern University study , this clothing can help prevent individuals' automated tracking via surveillance cameras.

Data scientists will need to defend against adversarial inputs like this. And provide trick examples for models to train on so as not to be fooled.

Adversarial training measures for models like this will become essential in the next decade.

Wrapping Up

Those are the 7 biggest data science trends over the next 3-4 years.

Data science, like any science, is changing by the day. From data governance to deepfake technology, the data science industry is set for some major shakeups.

Hopefully keeping tabs on these trends will help you stay one step ahead.

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Data science has a key role to play in climate change..

The science, policy, and communication practices around data science, machine learning, and artificial intelligence have important implications for the climate crisis and the solutions society will utilize in the future.

From machine learning to data visualization, data science techniques are used to study the effects of climate change on marine biology, land use and restoration, food systems, patterns of change in vector borne diseases, and other climate-related issues. Data science is a powerful tool to help researchers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize co-benefits for humanity and the environment, and to evaluate the multiple–and sometimes conflicting–goals of decision-makers.

DSI researchers use the methods and tools of the growing field of data science and apply them to issues relevant to climate change and the environment.

Our researchers combine techniques from data science and environmental science to understand patterns in the global food system and develop strategies that make food-supply chains more nutritious and sustainable. They look at how machine learning can reduce the uncertainty of climate models, use deep learning for climate model superresolution, and help visualize carbon emissions based on raw data. Some combine machine learning with simulations of atmospheric turbulence to develop new models that can track air pollutants and reconstruct 3D scalar fields from 2D satellite images. Others provide innovative training in environmental health sciences, including climate and health. Researchers developed a data collaborative to harness geospatial data to help characterize populations displaced by disaster. This kind of information may help with planning for and responding to large scale natural disasters associated with climate change.

DSI students complete capstone projects to apply data science techniques to real world problems. One recent project used climate data to predict heavy snowfall. Using a data set of regional climate simulations, the student team calculated the frequency of large snow storms and tracked how the storm statistics will change in the future. Another project used machine-learning methods to develop mapped estimates of surface ocean CO2 concentrations from the limited ocean data available to monitor carbon sink to predict climate change. We help our students interpret environmental data by teaching how data are managed, stored, and disseminated, as well as how they are enrolled into various narratives and models of climate change.

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Research Highlights

• Ryan Abernathey, Earth and Environmental Sciences
• Peter deMenocal, Earth and Environmental Sciences and Lamont-Doherty Earth Observatory
• Mu Xu, Lamont-Doherty Earth Observatory

Eddy transport of tracers (e.g. heat, salt, dissolved chemicals, etc.) by mesoscale turbulence is important in climate models. However, the scales of eddy transport are about 10~200 km, which are not resolved by coarse-resolution global climate models. Consequently, the mesoscale tracer transports must be parameterized using a subgrid scheme. The goal of parameterization is to predict the tendencies of physical variables including velocity, temperature, salinity etc. due to the unresolved turbulent motions. There are many different types of mesoscale subgrid schemes, and many different tuning parameters. However, evaluating the performance of subgrid scheme quantitatively is difficult. In this work, we present a framework to evaluate the accuracy of subgrid schemes quantitatively with a data-driven method. We run a high-resolution simulation with resolution of about 5 km and consider this as our “truth.” With a coarse-grain method, the high-resolution data is projected to a low-resolution grid. The quantitative aim of eddy parameterization is to mitigate the loss of tracer transport due to the coarse-graining. Based on this consideration, we develop an offline system to calculate the eddy parameterization predictions and evaluate the performance of different subgrid schemes. This work lays a foundation for future data-driven statistical-learning-based methods for ocean eddy tracer transport parameterization.

• Joaquim Goes, Lamont-Doherty Earth Observatory
• Ankit Peshin, Ziyao Zhang and Paridhi Singh, Data Science Institute

This research team travels by ship to different parts of the Atlantic Ocean to collect water samples to study the effects of climate change on marine biology. They are designing an automated system through which seawater may be drawn into their moving ship and continuously analyzed. This automated system is an advancement over the usual method of collecting samples; ocean researchers typically stop their ships at pre-planned locations to collect samples. Data is also being gathered on the diversity of microscopic plant life, particularly plankton, which are critical to the marine ecosystem and to assess the ocean’s ability to sequester carbon dioxide from the atmosphere. Plankton form the basis of many food chains and are an important indicator of an ocean’s health. When fully functional, the system will provide data required to validate satellite images of the ocean now being developed by NASA, NOAA and other agencies.

• Marco Giometto, Civil Engineering and Engineering Mechanics
• Pierre Gentine and Mostafa Momen, Earth and Environmental Engineering
• Carl Vondrick, Computer Science

Satellite images are routinely used to track pollutant dispersion in the atmosphere, but the inherently two-dimensional information is limited and often impedes the development of effective rapid response plans. This project will develop a machine learning model to predict the three-dimensional structure of pollutant concentrations from satellite images of the dispersion process. Machine learning will be combined with high-fidelity simulations of atmospheric turbulence to guide the development of a model to track scalar dispersion as well as a model to reconstruct the corresponding three-dimensional concentration field from two-dimensional satellite-like information.

• Zsuzsa Marka and Szabolcs Marka, Physics and Columbia Astrophysics Laboratory
• John Wright, Electrical Engineering
• Zelda Moran, Earth Institute

Control of tsetse flies — the vector responsible for African Trypanosomiasis or sleeping sickness — is highly dependent on precise, high-volume, and cost-effective separation of tsetse genders. Enabling broad deployment, this team is pioneering machine learning-based robotic systems that use infrared imaging to peek inside tsetse pupae for early, robust, and fast identification of males to be used to suppress the wild tsetse population.

• Ruth DeFries, Ecology, Evolution and Environmental Biology
• Walter Baethgen, International Research Institute for Climate and Society
• Michael Puma, Center for Climate Systems Research, NASA Goddard Institute for Space Studies
• Kyle Davis, Data Science Institute

This project combines field work with data-driven techniques to study how to improve patterns of food trade in Latin America, sub-Saharan Africa, and South and Southeast Asia. Food trade patterns are essentially the import and export links that connect the production of food in one country to the consumption of it in another. If, for instance, an exporting country experiences a shortage in food production, it may be unable to provide the usual amount of exports to its trade partners. This project assesses how vulnerable these exporting countries are to production shortfalls, and how importing countries may buffer themselves against these possible shortfalls so they are not adversely affected. Research in food-system sustainability aims to minimize the effects of food production on the environment, working with local residents and experts to adapt agricultural systems to protect the environment and mitigate climate change.

• Christoph Meinrenken, Earth Institute

Carbon Catalogue, a free online interactive tool, visualizes the carbon emissions in hundreds of consumer and commercial products around the world. Co-developed with CoClear, a Columbia-alumna founded sustainability analytics firm, Carbon Catalogue, is based on raw data from CDP (formerly the Carbon Disclosure Project). Its visualization features were further honed during a DSI hackathon.

• Robert Chen, Center for International Earth and Science Information

Network Groups tasked with planning for and responding to disasters and humanitarian crises contend with data that is often fragmented, delayed, and limited in reliability. This project focuses on setting up the Data Collaborative and Modalities of Communication involving key users from the Platform on Disaster Displacement, (PDD) Columbia University’s humanitarian research community, selected commercial providers, and other relevant data science organizations and experts. It also identifies the data/information needs of the humanitarian/displacement tracking community and develops pilot tests of selected data streams (Internet location, night-time lights, etc.).

• Gavin Schmidt, NASA Goddard Institute for Space Studies and Earth Institute

Every winter the news media covers stories of massive disruption caused by large snowfalls for which cities and counties in the Eastern U.S. are apparently ill-prepared. The impact of large snow events is worse in regions that rarely get them and the observational statistics of their likelihood are limited because of their rarity. Nonetheless, there are anticipated changes in these statistics because of two possible counterbalancing factors in climate change — overall warming which might reduce snow events at the southern edge of the region and greater intensity of precipitation and higher atmospheric water vapor content, which might increase heavy snowfalls. Using a 50 member regional climate model ensemble, we explore the statistics of highly impactful snowfalls and address where and when we might be able to detect and expect significant changes over time.

• Galen McKinley, Earth and Environmental Science and Lamont Doherty Earth Observatory

Climate is changing due to human emissions of carbon to the atmosphere. But not all the carbon emitted remains in the atmosphere. In fact, over the course of the industrial era, the ocean has absorbed the equivalent of 41 percent of all human fossil-fuel derived carbon dioxide emissions, a phenomenon known as “sink.” Studying the ocean carbon cycle is critical to understanding and predicting climate change. It is also essential for efforts to limit climate change by reducing the growth rate of atmospheric CO2 concentrations. Ocean data are quite sparse, and CO2 in water cannot be directly measured from space. This team uses machine-learning methods to develop mapped estimates of surface ocean CO2 concentrations from the limited data available. Interpreting Urban Environmental Data This pilot course is part of a broader rethinking of archaeology at Columbia. Faculty work towards developing a more integrated archaeology program to bring together expertise and resources from different departments and develop a course which may be offered at the M.A. level and better serve our undergraduate population. The course also provides training in historical terrestrial palaeoecology and cultivates an informed historical consciousness and an understanding of the wider repercussions beyond the field of scientific research and reporting.

Data Science Minor

Undergraduate research.

Many students who pursue the data science minor hope to put their data science skills to practice before graduation. Whether you are interested in researching a data science topic, or simply want to put your skills to practice, undergraduate research can be highly beneficial experience. 

On This Page 

• Data Science as a Research Topic 
• Data Science as a Skill for Research 

Pathways into Undergraduate Research 

• Scope of Research Experience 

Envisioning a Research Experience

• Helpful Terminology
• Finding Research Opportunities
• Reaching Out to Faculty 
• Data Science Research Skills

Data Science as a Research Topic  

Data science as a research topic involves investigating data science itself and contributes to the knowledge and understanding of data science methods and principles. In other words, researchers are exploring questions, problems, or topics related to data science. This can involve theoretical or empirical research on data science methodologies, algorithms, tools, best practices, or emerging trends. Research topics in data science might include:

• Development and optimization of machine learning algorithms.
• Evaluation of data mining techniques for specific applications.
• Studies on data preprocessing and feature engineering.
• Ethical considerations in data science and AI.
• Investigations into the social, economic, or policy implications of data science and big data.

Researchers working on data science as a research topic aim to advance the field itself, contribute to the knowledge base, and often publish their findings in academic journals or conferences specific to data science and related disciplines.

Data Science as a Skill for Research

Data science as a skill for research involves using data science techniques and tools as a means to conduct research in other fields, applying data science methods to answer questions or address problems within a specific discipline. Researchers use data science techniques to collect, analyze, and derive insights from data that are relevant to their primary area of study. Data science skills are considered a means to an end in this context, rather than the main focus of research. Examples include:

• A biologist using data science methods to analyze genetic data.
• An economist using data science for economic modeling and forecasting.
• A sociologist using data science for social network analysis.
• A psychologist using data science for analyzing behavioral data.
• A public health researcher using data science to analyze epidemiological data.

In these cases, data science skills are applied to enhance the quality and depth of research within a specific discipline. The primary goal is to answer research questions or address issues in the primary domain of study, and data science is a tool to achieve that goal.

There are two primary pathways for undergraduate students to get started in research - independently reaching out to a mentor or getting started through an organized program. Independent outreach is the most common way students get involved in research. This involves identifying a research mentor/faculty whose research aligns with your interests and reaching out to them through email. Students can also apply to an organized program to get involved in research. Organized programs have specific application processes, deadlines, and expectations for each program. To learn more, visit the Office of Undergraduate Research How to Get Started page or read on for more information specific to data science minors. 

Office of Undergraduate Research How to Get Started 

Scope of Research Experience

Undergraduate students engage in reseach at all points of the research process. Some may be interested in researching their own question and engaging in a full research cycle (following a project from start to finish), while others may be interested in working on a project already in motion or at a particular point in the research process. Both independent outreach and organized programs can provide students with opportunites that fit their desired research experience and goals. Many structured/organized undergraduate research programs support students who are working extremely independently. 

It is important to reflect on why you are interested in research and your goals for the experience. Before you start exploring opportunities, reflect on the following: 

• Independence:  How much independence are you looking for - both in the research experience and the research topic/project? Some experiences may offer a higher degree of independence in choosing the research topic, designing experiments, and conducting research. Do you want to experience a full research cycle (working on a project from start to finish) or be part of a particular step in the research process? Do you want to be part of a larger research program with defined goals and objectives?
• Flexibility: How much flexibility are you looking for - both in terms of time committment and research topic? Students working 1:1 with a faculty member may have more flexibility in terms of the research project's focus, timelines, and methodologies. They may be able to explore areas of personal interest. Structured programs may provide students with specific program timelines (i.e. summer experiences) and hour requirements. How much time each week do you have to dedicate to research? What other committments do you have?
• Faculty Mentor:  All students will work with a faculty mentor who guides and advises them throughout the research process. The mentor provides support, reviews progress, and offers expertise. What characterics do you want in a research mentor? What questions could you ask a faculty member to better understand their mentorship philosophy?
• Credit-Based:  Many students hope to earn academic credit for their work. Is this something that is important to you? Do you hope to use this credit to count for major or minor requirements? 
• Duration: The duration of research projects can vary widely, from a single quarter to multiple years, depending on the student's goals and availability. Some structured programs may have a set duration, such as a summer research program, which typically lasts for a few months. Sometimes it is possible for a student to work with a faculty member for multiple years as an undergrad, and go on to work in a full time capacity in the lab. Reflect on your long term goals for the work and communicate those with potential mentors. 
• Resources/Getting Paid: Different research opportunities will have access to different types of research facilities and resources. Do you want to get paid? Independent outreach may lead to more frequent unpaid opportunities compared to structured/funded programs, but there are still many ways to fund your work. Structured programs often provide more resources, including funding, equipment, and facilities.
• Research Setting:  What type of setting are you interested in? A lab, field work, library, virtual, hospital, community organization, non-profit, clinic, etc? If you don’t know what you may like, how could you gain more insight?
• Research Topic: Is there a particular academic topic or question you are hoping to investigate? Are you open to the topic and more interested in the research skill you might get to practice? 
• Publication and Presentation: Are you interested in presenting and publishing your work? Talk with your faculty mentor to explore options in this area. All students are encouraged to present at the Undergraduate Research Symposium.  
• Personal Goals: What other goals do you have for getting involved? What skills are you hoping to practice or learn? Reflecting on your personal goals will help your faculty mentor help you get the most out of your experience. 

Helpful Terminology 

PI: Principle Investigator (person leading the research)

Research Center/Institute

• Founded and funded for doing research, group of labs/projects
• Exists at universities, hospitals, non-profits, government, think tanks, etc.
• Multidisciplinary, works across disciplines
• Research centers tend to engage in long-term, ongoing research efforts, often spanning multiple projects

Research Lab/Group 

• A research lab is a smaller, more specialized facility or unit within a research center, university department, or organization
• They may be run by a principal investigator or lead researcher and consist of a team of researchers, technicians, and students
• Research labs are more project-oriented and often have a defined timeline for their research efforts

Research Project

• A research project is a specific, time-limited endeavor with a well-defined research question, objectives, and scope
• It can be conducted within a research lab, research center, or as an independent effort
• Research projects have a specific duration and are designed to answer a particular research question or solve a specific problem

Finding Research Opportunities 

Often the most time consuming part of undergradaute research is learning what opportunities exist. It's time to put on your researcher hat and spend some time online doing research into faculty whose work aligns with your interests and what structured progrIn addition to the resources below, the Office of Undergraduate Research has a great Getting Started page. 

How to Find Research Opportunities 

Office of Undergraduate Research  Office of Undergraduate Research  Undergraduate Research Database  Undergraduate Research Symposium Schedule

UW Research Centers UW Research Centers

Departmental Websites Most departmental websites will have a research section that shares current and past projects. Search UW "department name", then look for a research tab at the top.  Example: Psychology,  Econ

College of Arts and Sciences Research  Research in the College of Arts and Sciences

Structured Undergraduate Research Programs Academic Year Programs Mary Gates Research Scholarship  Levinson Emerging Scholars Award Washington Research Foundation Fellowships Beyond UW Summer Programs  URP Co-Hosted Summer Programs Summer Research Programs

Data Science as a Research Topic  Allen School Data Science Data Management and Visualization UW Database Group   Informatics HCDE eScience Institute UW Database Group

Additional Data Science Programs (may be non-research)  DSI Summer Lab - University of Chicago  Data Science Summer Course, American University of Armenia Wharton Data Science Academy Summer Program Microsoft Research Data Science Summer School Hoya Summer High School Sessions, Georgetown University The University of Chicago Summer Session Data Science Summer Institute Analytics, Data Science & Decision Making Virtual Summer School Jump-Start Data Science Summer Program, William and Mary Essex Analytics, Data Science and Decision Making Online Summer School

Reaching Out to a Faculty Mentor about Research Opportunities 

Once you have identified a faculty whose work you are intersted in, it's time to reach out. Reaching out can be intimitating. The Office of Undergraduate Research has a website to support you in this process. 

Reach Out to a Mentor 

Data Science Research Skills 

Data science skills are highly valuable in research across various disciplines. These skills help researchers collect, analyze, and interpret data, allowing them to draw meaningful insights and make evidence-based conclusions. Prior to engaging in research, reflect on the skills you have experience with, the skills you hope to practice during the research experience, and the skills you hope to gain. This reflection will help your faculty mentor best support you. Intrapersonal, communication, and critical thinking skills are just, if not more, important as the technical skills. Demonstrated curiosity in the research process can not be overstated. 

Statistical Analysis

• Researchers need a strong foundation in statistics to perform hypothesis testing, regression analysis, and other statistical techniques to extract patterns and relationships from data.

Data Collection

• The ability to gather, clean, and preprocess data is crucial. This includes skills in data extraction, data cleaning, and data transformation.

Data Visualization

• Data visualization skills help researchers communicate their findings effectively. Proficiency in tools like Matplotlib, ggplot2, or Tableau can make complex data more accessible.

Machine Learning

• Machine learning algorithms are used to build predictive models and classify data. Researchers may need to apply techniques like decision trees, support vector machines, or neural networks.

Data Mining

• Researchers can use data mining techniques to discover patterns, anomalies, and relationships within large datasets. 

Programming

• Proficiency in programming languages like Python or R is essential for data manipulation, analysis, and scripting.

Database Management

• Knowledge of database systems, including SQL, is necessary for working with structured data and querying databases for research purposes.

Big Data Technologies

• Familiarity with big data tools is important for handling and processing large datasets.

Data Ethics

• Ethical considerations are crucial in research, especially when handling sensitive or personal data. Researchers should be aware of privacy regulations and ethical data practices.

Data Storytelling

• Researchers need to communicate their findings effectively, which includes the ability to create compelling narratives from data.

Domain/Industry Knowledge

• Understanding the specific industry or field of research is essential for framing research questions, selecting relevant data sources, and interpreting results accurately.

Experiment Design

• In experimental research, knowledge of experimental design principles is essential for planning, conducting, and analyzing experiments.

Time Series Analysis

• Time series analysis helps researchers understand the underlying causes of trends or systemic patterns over time. 

Geospatial Analysis

• Geospatial analysis is used to add timing and location information to traditional types of data and to build data visualizations.

Text Analysis

• For research involving text data, natural language processing (NLP) and text mining skills are valuable for extracting insights from textual content.

Data Security

• Researchers need to ensure the security and integrity of research data, especially in studies involving sensitive or confidential information.

Version Control

• Using version control systems help track and manage changes to software code, ensuring reproducibility and transparency in research 

Collaboration and Communication

• Effective communication and collaboration skills are essential for sharing findings, collaborating with peers, and presenting research results.

Leverage familiarity with data science in fields outside of data science, and gain skills and fluency to work with data in your major domain of study.

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Five Key Trends in AI and Data Science for 2024

These developing issues should be on every leader’s radar screen, data executives say.

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Artificial intelligence and data science became front-page news in 2023. The rise of generative AI, of course, drove this dramatic surge in visibility. So, what might happen in the field in 2024 that will keep it on the front page? And how will these trends really affect businesses?

During the past several months, we’ve conducted three surveys of data and technology executives. Two involved MIT’s Chief Data Officer and Information Quality Symposium attendees — one sponsored by Amazon Web Services (AWS) and another by Thoughtworks . The third survey was conducted by Wavestone , formerly NewVantage Partners, whose annual surveys we’ve written about in the past . In total, the new surveys involved more than 500 senior executives, perhaps with some overlap in participation.

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Surveys don’t predict the future, but they do suggest what those people closest to companies’ data science and AI strategies and projects are thinking and doing. According to those data executives, here are the top five developing issues that deserve your close attention:

1. Generative AI sparkles but needs to deliver value.

As we noted, generative AI has captured a massive amount of business and consumer attention. But is it really delivering economic value to the organizations that adopt it? The survey results suggest that although excitement about the technology is very high , value has largely not yet been delivered. Large percentages of respondents believe that generative AI has the potential to be transformational; 80% of respondents to the AWS survey said they believe it will transform their organizations, and 64% in the Wavestone survey said it is the most transformational technology in a generation. A large majority of survey takers are also increasing investment in the technology. However, most companies are still just experimenting, either at the individual or departmental level. Only 6% of companies in the AWS survey had any production application of generative AI, and only 5% in the Wavestone survey had any production deployment at scale.

Surveys suggest that though excitement about generative AI is very high, value has largely not yet been delivered.

Production deployments of generative AI will, of course, require more investment and organizational change, not just experiments. Business processes will need to be redesigned, and employees will need to be reskilled (or, probably in only a few cases, replaced by generative AI systems). The new AI capabilities will need to be integrated into the existing technology infrastructure.

Perhaps the most important change will involve data — curating unstructured content, improving data quality, and integrating diverse sources. In the AWS survey, 93% of respondents agreed that data strategy is critical to getting value from generative AI, but 57% had made no changes to their data thus far.

2. Data science is shifting from artisanal to industrial.

Companies feel the need to accelerate the production of data science models . What was once an artisanal activity is becoming more industrialized. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. MLOps systems monitor the status of machine learning models and detect whether they are still predicting accurately. If they’re not, the models might need to be retrained with new data.

Producing data models — once an artisanal activity — is becoming more industrialized.

Most of these capabilities come from external vendors, but some organizations are now developing their own platforms. Although automation (including automated machine learning tools, which we discuss below) is helping to increase productivity and enable broader data science participation, the greatest boon to data science productivity is probably the reuse of existing data sets, features or variables, and even entire models.

3. Two versions of data products will dominate.

In the Thoughtworks survey, 80% of data and technology leaders said that their organizations were using or considering the use of data products and data product management. By data product , we mean packaging data, analytics, and AI in a software product offering, for internal or external customers. It’s managed from conception to deployment (and ongoing improvement) by data product managers. Examples of data products include recommendation systems that guide customers on what products to buy next and pricing optimization systems for sales teams.

But organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all.

We have a slight preference for a definition of data products that includes analytics and AI, since that is the way data is made useful. But all that really matters is that an organization is consistent in how it defines and discusses data products. If an organization prefers a combination of “data products” and “analytics and AI products,” that can work well too, and that definition preserves many of the positive aspects of product management. But without clarity on the definition, organizations could become confused about just what product developers are supposed to deliver.

4. Data scientists will become less sexy.

Data scientists, who have been called “ unicorns ” and the holders of the “ sexiest job of the 21st century ” because of their ability to make all aspects of data science projects successful, have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative.

Another factor reducing the demand for professional data scientists is the rise of citizen data science , wherein quantitatively savvy businesspeople create models or algorithms themselves. These individuals can use AutoML, or automated machine learning tools, to do much of the heavy lifting. Even more helpful to citizens is the modeling capability available in ChatGPT called Advanced Data Analysis . With a very short prompt and an uploaded data set, it can handle virtually every stage of the model creation process and explain its actions.

Of course, there are still many aspects of data science that do require professional data scientists. Developing entirely new algorithms or interpreting how complex models work, for example, are tasks that haven’t gone away. The role will still be necessary but perhaps not as much as it was previously — and without the same degree of power and shimmer.

5. Data, analytics, and AI leaders are becoming less independent.

This past year, we began to notice that increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” including chief data and analytics officers (and sometimes chief AI officers). That CDO/CDAO role, while becoming more common in companies, has long been characterized by short tenures and confusion about the responsibilities. We’re not seeing the functions performed by data and analytics executives go away; rather, they’re increasingly being subsumed within a broader set of technology, data, and digital transformation functions managed by a “supertech leader” who usually reports to the CEO. Titles for this role include chief information officer, chief information and technology officer, and chief digital and technology officer; real-world examples include Sastry Durvasula at TIAA, Sean McCormack at First Group, and Mojgan Lefebvre at Travelers.

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This evolution in C-suite roles was a primary focus of the Thoughtworks survey, and 87% of respondents (primarily data leaders but some technology executives as well) agreed that people in their organizations are either completely, to a large degree, or somewhat confused about where to turn for data- and technology-oriented services and issues. Many C-level executives said that collaboration with other tech-oriented leaders within their own organizations is relatively low, and 79% agreed that their organization had been hindered in the past by a lack of collaboration.

We believe that in 2024, we’ll see more of these overarching tech leaders who have all the capabilities to create value from the data and technology professionals reporting to them. They’ll still have to emphasize analytics and AI because that’s how organizations make sense of data and create value with it for employees and customers. Most importantly, these leaders will need to be highly business-oriented, able to debate strategy with their senior management colleagues, and able to translate it into systems and insights that make that strategy a reality.

About the Authors

Thomas H. Davenport ( @tdav ) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (HBR Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean ( @randybeannvp ) is an industry thought leader, author, founder, and CEO and currently serves as innovation fellow, data strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

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214 Best Big Data Research Topics for Your Thesis Paper

Finding an ideal big data research topic can take you a long time. Big data, IoT, and robotics have evolved. The future generations will be immersed in major technologies that will make work easier. Work that was done by 10 people will now be done by one person or a machine. This is amazing because, in as much as there will be job loss, more jobs will be created. It is a win-win for everyone.

Big data is a major topic that is being embraced globally. Data science and analytics are helping institutions, governments, and the private sector. We will share with you the best big data research topics.

On top of that, we can offer you the best writing tips to ensure you prosper well in your academics. As students in the university, you need to do proper research to get top grades. Hence, you can consult us if in need of research paper writing services.

Big Data Analytics Research Topics for your Research Project

Are you looking for an ideal big data analytics research topic? Once you choose a topic, consult your professor to evaluate whether it is a great topic. This will help you to get good grades.

• Which are the best tools and software for big data processing?
• Evaluate the security issues that face big data.
• An analysis of large-scale data for social networks globally.
• The influence of big data storage systems.
• The best platforms for big data computing.
• The relation between business intelligence and big data analytics.
• The importance of semantics and visualization of big data.
• Analysis of big data technologies for businesses.
• The common methods used for machine learning in big data.
• The difference between self-turning and symmetrical spectral clustering.
• The importance of information-based clustering.
• Evaluate the hierarchical clustering and density-based clustering application.
• How is data mining used to analyze transaction data?
• The major importance of dependency modeling.
• The influence of probabilistic classification in data mining.

Interesting Big Data Analytics Topics

Who said big data had to be boring? Here are some interesting big data analytics topics that you can try. They are based on how some phenomena are done to make the world a better place.

• Discuss the privacy issues in big data.
• Evaluate the storage systems of scalable in big data.
• The best big data processing software and tools.
• Data mining tools and techniques are popularly used.
• Evaluate the scalable architectures for parallel data processing.
• The major natural language processing methods.
• Which are the best big data tools and deployment platforms?
• The best algorithms for data visualization.
• Analyze the anomaly detection in cloud servers
• The scrutiny normally done for the recruitment of big data job profiles.
• The malicious user detection in big data collection.
• Learning long-term dependencies via the Fourier recurrent units.
• Nomadic computing for big data analytics.
• The elementary estimators for graphical models.
• The memory-efficient kernel approximation.

Big Data Latest Research Topics

Do you know the latest research topics at the moment? These 15 topics will help you to dive into interesting research. You may even build on research done by other scholars.

• Evaluate the data mining process.
• The influence of the various dimension reduction methods and techniques.
• The best data classification methods.
• The simple linear regression modeling methods.
• Evaluate the logistic regression modeling.
• What are the commonly used theorems?
• The influence of cluster analysis methods in big data.
• The importance of smoothing methods analysis in big data.
• How is fraud detection done through AI?
• Analyze the use of GIS and spatial data.
• How important is artificial intelligence in the modern world?
• What is agile data science?
• Analyze the behavioral analytics process.
• Semantic analytics distribution.
• How is domain knowledge important in data analysis?

Big Data Debate Topics

If you want to prosper in the field of big data, you need to try even hard topics. These big data debate topics are interesting and will help you to get a better understanding.

• The difference between big data analytics and traditional data analytics methods.
• Why do you think the organization should think beyond the Hadoop hype?
• Does the size of the data matter more than how recent the data is?
• Is it true that bigger data are not always better?
• The debate of privacy and personalization in maintaining ethics in big data.
• The relation between data science and privacy.
• Do you think data science is a rebranding of statistics?
• Who delivers better results between data scientists and domain experts?
• According to your view, is data science dead?
• Do you think analytics teams need to be centralized or decentralized?
• The best methods to resource an analytics team.
• The best business case for investing in analytics.
• The societal implications of the use of predictive analytics within Education.
• Is there a need for greater control to prevent experimentation on social media users without their consent?
• How is the government using big data; for the improvement of public statistics or to control the population?

University Dissertation Topics on Big Data

Are you doing your Masters or Ph.D. and wondering the best dissertation topic or thesis to do? Why not try any of these? They are interesting and based on various phenomena. While doing the research ensure you relate the phenomenon with the current modern society.

• The machine learning algorithms are used for fall recognition.
• The divergence and convergence of the internet of things.
• The reliable data movements using bandwidth provision strategies.
• How is big data analytics using artificial neural networks in cloud gaming?
• How is Twitter accounts classification done using network-based features?
• How is online anomaly detection done in the cloud collaborative environment?
• Evaluate the public transportation insights provided by big data.
• Evaluate the paradigm for cancer patients using the nursing EHR to predict the outcome.
• Discuss the current data lossless compression in the smart grid.
• How does online advertising traffic prediction helps in boosting businesses?
• How is the hyperspectral classification done using the multiple kernel learning paradigm?
• The analysis of large data sets downloaded from websites.
• How does social media data help advertising companies globally?
• Which are the systems recognizing and enforcing ownership of data records?
• The alternate possibilities emerging for edge computing.

The Best Big Data Analysis Research Topics and Essays

There are a lot of issues that are associated with big data. Here are some of the research topics that you can use in your essays. These topics are ideal whether in high school or college.

• The various errors and uncertainty in making data decisions.
• The application of big data on tourism.
• The automation innovation with big data or related technology
• The business models of big data ecosystems.
• Privacy awareness in the era of big data and machine learning.
• The data privacy for big automotive data.
• How is traffic managed in defined data center networks?
• Big data analytics for fault detection.
• The need for machine learning with big data.
• The innovative big data processing used in health care institutions.
• The money normalization and extraction from texts.
• How is text categorization done in AI?
• The opportunistic development of data-driven interactive applications.
• The use of data science and big data towards personalized medicine.
• The programming and optimization of big data applications.

The Latest Big Data Research Topics for your Research Proposal

Doing a research proposal can be hard at first unless you choose an ideal topic. If you are just diving into the big data field, you can use any of these topics to get a deeper understanding.

• The data-centric network of things.
• Big data management using artificial intelligence supply chain.
• The big data analytics for maintenance.
• The high confidence network predictions for big biological data.
• The performance optimization techniques and tools for data-intensive computation platforms.
• The predictive modeling in the legal context.
• Analysis of large data sets in life sciences.
• How to understand the mobility and transport modal disparities sing emerging data sources?
• How do you think data analytics can support asset management decisions?
• An analysis of travel patterns for cellular network data.
• The data-driven strategic planning for citywide building retrofitting.
• How is money normalization done in data analytics?
• Major techniques used in data mining.
• The big data adaptation and analytics of cloud computing.
• The predictive data maintenance for fault diagnosis.

Interesting Research Topics on A/B Testing In Big Data

A/B testing topics are different from the normal big data topics. However, you use an almost similar methodology to find the reasons behind the issues. These topics are interesting and will help you to get a deeper understanding.

• How is ultra-targeted marketing done?
• The transition of A/B testing from digital to offline.
• How can big data and A/B testing be done to win an election?
• Evaluate the use of A/B testing on big data
• Evaluate A/B testing as a randomized control experiment.
• How does A/B testing work?
• The mistakes to avoid while conducting the A/B testing.
• The most ideal time to use A/B testing.
• The best way to interpret results for an A/B test.
• The major principles of A/B tests.
• Evaluate the cluster randomization in big data
• The best way to analyze A/B test results and the statistical significance.
• How is A/B testing used in boosting businesses?
• The importance of data analysis in conversion research
• The importance of A/B testing in data science.

Amazing Research Topics on Big Data and Local Governments

Governments are now using big data to make the lives of the citizens better. This is in the government and the various institutions. They are based on real-life experiences and making the world better.

• Assess the benefits and barriers of big data in the public sector.
• The best approach to smart city data ecosystems.
• The big analytics used for policymaking.
• Evaluate the smart technology and emergence algorithm bureaucracy.
• Evaluate the use of citizen scoring in public services.
• An analysis of the government administrative data globally.
• The public values are found in the era of big data.
• Public engagement on local government data use.
• Data analytics use in policymaking.
• How are algorithms used in public sector decision-making?
• The democratic governance in the big data era.
• The best business model innovation to be used in sustainable organizations.
• How does the government use the collected data from various sources?
• The role of big data for smart cities.
• How does big data play a role in policymaking?

Easy Research Topics on Big Data

Who said big data topics had to be hard? Here are some of the easiest research topics. They are based on data management, research, and data retention. Pick one and try it!

• Who uses big data analytics?
• Evaluate structure machine learning.
• Explain the whole deep learning process.
• Which are the best ways to manage platforms for enterprise analytics?
• Which are the new technologies used in data management?
• What is the importance of data retention?
• The best way to work with images is when doing research.
• The best way to promote research outreach is through data management.
• The best way to source and manage external data.
• Does machine learning improve the quality of data?
• Describe the security technologies that can be used in data protection.
• Evaluate token-based authentication and its importance.
• How can poor data security lead to the loss of information?
• How to determine secure data.
• What is the importance of centralized key management?

Unique IoT and Big Data Research Topics

Internet of Things has evolved and many devices are now using it. There are smart devices, smart cities, smart locks, and much more. Things can now be controlled by the touch of a button.

• Evaluate the 5G networks and IoT.
• Analyze the use of Artificial intelligence in the modern world.
• How do ultra-power IoT technologies work?
• Evaluate the adaptive systems and models at runtime.
• How have smart cities and smart environments improved the living space?
• The importance of the IoT-based supply chains.
• How does smart agriculture influence water management?
• The internet applications naming and identifiers.
• How does the smart grid influence energy management?
• Which are the best design principles for IoT application development?
• The best human-device interactions for the Internet of Things.
• The relation between urban dynamics and crowdsourcing services.
• The best wireless sensor network for IoT security.
• The best intrusion detection in IoT.
• The importance of big data on the Internet of Things.

Big Data Database Research Topics You Should Try

Big data is broad and interesting. These big data database research topics will put you in a better place in your research. You also get to evaluate the roles of various phenomena.

• The best cloud computing platforms for big data analytics.
• The parallel programming techniques for big data processing.
• The importance of big data models and algorithms in research.
• Evaluate the role of big data analytics for smart healthcare.
• How is big data analytics used in business intelligence?
• The best machine learning methods for big data.
• Evaluate the Hadoop programming in big data analytics.
• What is privacy-preserving to big data analytics?
• The best tools for massive big data processing
• IoT deployment in Governments and Internet service providers.
• How will IoT be used for future internet architectures?
• How does big data close the gap between research and implementation?
• What are the cross-layer attacks in IoT?
• The influence of big data and smart city planning in society.
• Why do you think user access control is important?

Big Data Scala Research Topics

Scala is a programming language that is used in data management. It is closely related to other data programming languages. Here are some of the best scala questions that you can research.

• Which are the most used languages in big data?
• How is scala used in big data research?
• Is scala better than Java in big data?
• How is scala a concise programming language?
• How does the scala language stream process in real-time?
• Which are the various libraries for data science and data analysis?
• How does scala allow imperative programming in data collection?
• Evaluate how scala includes a useful REPL for interaction.
• Evaluate scala’s IDE support.
• The data catalog reference model.
• Evaluate the basics of data management and its influence on research.
• Discuss the behavioral analytics process.
• What can you term as the experience economy?
• The difference between agile data science and scala language.
• Explain the graph analytics process.

Independent Research Topics for Big Data

These independent research topics for big data are based on the various technologies and how they are related. Big data will greatly be important for modern society.

• The biggest investment is in big data analysis.
• How are multi-cloud and hybrid settings deep roots?
• Why do you think machine learning will be in focus for a long while?
• Discuss in-memory computing.
• What is the difference between edge computing and in-memory computing?
• The relation between the Internet of things and big data.
• How will digital transformation make the world a better place?
• How does data analysis help in social network optimization?
• How will complex big data be essential for future enterprises?
• Compare the various big data frameworks.
• The best way to gather and monitor traffic information using the CCTV images
• Evaluate the hierarchical structure of groups and clusters in the decision tree.
• Which are the 3D mapping techniques for live streaming data.
• How does machine learning help to improve data analysis?
• Evaluate DataStream management in task allocation.
• How is big data provisioned through edge computing?
• The model-based clustering of texts.
• The best ways to manage big data.
• The use of machine learning in big data.

Is Your Big Data Thesis Giving You Problems?

These are some of the best topics that you can use to prosper in your studies. Not only are they easy to research but also reflect on real-time issues. Whether in University or college, you need to put enough effort into your studies to prosper. However, if you have time constraints, we can provide professional writing help. Are you looking for online expert writers? Look no further, we will provide quality work at a cheap price.

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Fundamental Mathematical Topics in Data Science

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Since the turn of the century, there has been a surge of interest in research on data science. Techniques related to data science have become the main driving force behind numerous areas of industry and many new research directions have been developed, with new scientific questions raised from the study of ...

Keywords : sparse representation, reproducing kernels, machine learning, image processing, non-convex optimization

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Modern research, and the tools available to today's medical researchers, generate large, diverse, complex data sets. Our research communities have become a data-intense enterprise, representing everything from disease pathogenesis to information about trends, associations and connections between points of patient data. These studies represent some of the work being done as part of the NIH Social, Behavioral and Economic Impacts of COVID-19 initiative to produce findings and solutions from multiple sets of data in order to help understand and address the broader effects of the epidemic. 

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Utilizing the UNC Clinical Data Warehouse for Assessing Efficacy, Toxicity, and Dose-Response Relationships of Molecularly Targeted Therapies in Real-World Patients

CERSI Collaborators:  Triangle CERSI, UNC-Chapel Hill: Yanguang Cao, PhD; Benyam Muluneh, PharmD

FDA Collaborators: Mirat Shah, MD; Elizabeth Duke, MD; Jonathon Vellejo, PhD; Kun Wang, PhD; Lori Ehrlich, MD, PhD; Donna Rivera PharmD, MS

CERSI Subcontractors: Flying Buttress Associates- Jeph Herrin, PhD

CERSI In-Kind Collaborators: Joshua Zeidner, MD; Rushabh Shah, PharmD, MBA

Non-Federal Entity Collaborators: Johnson and Johnson- Karla Childers, MSJ, Paul Coplan, ScD, MBA, Stephen Johnston, MSc

Project Start Date: September 1, 2023

Regulatory Science Framework:

Modernize Development & Evaluation of FDA-Regulated Products, Methods to Assess Real-World Data to serve as Real-World Evidence; Strengthen Post-market Surveillance & Labeling of FDA-Regulated Products, Methods to Assess Real-World Data to Support Regulatory Decision Making

Regulatory Science Challenge

The doses and schedules for molecularly targeted therapies are often not well-characterized prior to initiating trials with registrational intent for marketing approval. This can lead to the selection of dosages that have increased toxicity without additional efficacy. Therapies may be poorly tolerated at approved dosages, leading patients or providers to prematurely modify the dosage or discontinue an otherwise efficacious treatment. FDA launched Project Optimus to emphasize the need for dosage optimization prior to drug approval. An understanding of how the toxicities and dosage modifications for approved targeted therapies affect outcomes for patients in the real world setting will enhance Project Optimus efforts, in collaboration with the Oncology Real World Evidence program , to advance potential patient-centric uses of real-world data. To bridge this knowledge gap, researchers will investigate the prevalence of dosage modifications in patients and the resulting safety and effectiveness profiles of targeted therapies commonly prescribed for patients with Chronic Lymphocytic Leukemia (CLL) and Chronic Myeloid Leukemia (CML). They will primarily use real-world data (RWD) sourced from the Clinical Data Warehouse at the University of North Carolina at Chapel Hill to address these questions.

Project Description and Goals

In this project, researchers will address three key questions. Firstly, they will assess the frequency of dosage modifications in patients in the real-world setting using pharmacy refill records and electronic medical record information. Secondly, they will aim to determine whether modifications in treatment doses, made in response to tolerability, have any negative impact on clinical outcomes including survival. Lastly, they will explore whether the toxicity associated with treatment directly affects clinical outcomes including survival among patients in the real-world setting. The clinical data warehouse at UNC Health Care System will be primarily utilized for this project, and Triangle CERSI is also considering other sources of real-world data, such as medical claims data and randomized clinical trials, where appropriate to support the analyses. Recognizing the potential challenges and biases that can be associated with RWD studies, the capabilities of statistical modeling and advanced data mining and causal inference approaches will be harnessed to explore methodological approaches for providing valid answers to these questions.

Research Outcomes/Results

Two hundred and twenty-three patients with a mean age of 65 years completed the survey. These patients preferred a higher chance of good biopsy outcomes, and a lower chance of erectile dysfunction caused by the treatment and urinary incontinence after treatment. The patients stated in the survey that they are willing to accept:

• a 15.1%-point increase in erectile dysfunction caused by the treatment to achieve a 10%-point increase in a good biopsy outcome after HIFU ablation, and
• an 8.5%-point increase in urinary incontinence for a 10%-point increase in a good biopsy.

Also, further analysis revealed that patients who thought their cancer was more aggressive were more willing to tolerate urinary incontinence. Younger men were willing to tolerate less erectile dysfunction risk than older men. Respondents with a greater than college level of education were less willing to tolerate erectile dysfunction or urinary incontinence.

Research Impacts

Incorporating patient preference information into decisions that FDA makes about regulating devices is one of the major goals of FDA’s Center for Devices and Radiological Health (CDRH). Study findings show that patients prefer specific outcomes related to prostate ablation therapies like HIFU. The study results may help inform the design and regulation of current and future prostate tissue ablation devices by providing information about outcomes that patients most desire.

Publications

• PMID: 34677594; Citation: Wallach JD, Deng Y, McCoy RG, Dhruva SS, Herrin J, Berkowitz A, Polley EC, Quinto K, Gandotra C, Crown W, Noseworthy P, Yao X, Shah ND, Ross JS, Lyon TD. Real-world Cardiovascular Outcomes Associated With Degarelix vs Leuprolide for Prostate Cancer Treatment.  JAMA Netw Open. 2021;4(10):e2130587. doi:10.1001/jamanetworkopen.2021.30587 .
• PMID: 36191949; Citation: Deng Y, Polley EC, Wallach JD, Dhruva SS, Herrin J, Quinto K, Gandotra C, Crown W, Noseworthy P, Yao X, Lyon TD, Shah ND, Ross JS, McCoy RG. Emulating the GRADE trial using real world data: retrospective comparative effectiveness study. BMJ . 2022 Oct 3;379:e070717. doi: 10.1136/bmj-2022-070717 .

Apply  to be a D-Lab Data Science Fellow! Attend our  infosession  for more details. Applications are due May 5.

Transparency in Experimental Political Science Research

With the increase in experimental studies in political science research, there are concerns about research transparency, particularly around reporting results from studies that contradict or do not find evidence for proposed theories (commonly called “null results”). One of these concerns is called p-hacking or the process of running many statistical analyses until results turn out to support a theory. A publication bias towards only publishing results with statistically significant results (or results that provide strong empirical evidence for a theory) has long encouraged p-hacking of data. 

To prevent p-hacking and encourage the publication of results with null results, political scientists have turned to pre-registering their experiments, be it online survey experiments or large-scale experiments conducted in the field. Many platforms are used to pre-register experiments and make research data available, such as OSF and Evidence in Governance and Politics (EGAP). An additional advantage of pre-registering analyses and data is that other researchers can attempt to replicate the results of studies, furthering the goal of research transparency.

For researchers, pre-registering experiments can be helpful in thinking about the research question and theory, the observable implications and hypotheses that arise from the theory, and the ways in which the hypotheses can be tested. As a political scientist who does experimental research, the process of pre-registration has been helpful for me in designing surveys and coming up with the appropriate methodologies to test my research questions. So, how do we pre-register a study and why might that be useful? In this blog post, I first show how to pre-register a study on OSF and provide resources to file a pre-registration. I then demonstrate research transparency in practice by distinguishing the analyses that I pre-registered in a recently completed study on misinformation and analyses that I did not pre-register that were exploratory in nature.

Research Question: Peer-to-Peer Correction of Misinformation

My co-author and I were interested in knowing how we can incentivize peer-to-peer correction of misinformation. Our research question was motivated by two facts:

• There is a growing distrust of media and government, particularly when it comes to technology.
• Though many interventions had been introduced to counter misinformation, these interventions were expensive and not scalable.

In order to counter misinformation, the most sustainable and scalable intervention would be for users to correct each other when they encounter misinformation online.

We proposed the use of social norm nudges – suggesting that misinformation correction was both acceptable and the responsibility of social media users – to encourage peer-to-peer correction of misinformation. We used a source of political misinformation on climate change and a source of non-political misinformation on microwaving a penny to get a “mini-penny”. We pre-registered all our hypotheses, the variables we were interested in, and the proposed analyses on OSF prior to collecting and analyzing our data.

Pre-Registering Studies on OSF

To start the process of pre-registration, researchers can create an OSF account for free and start a new project from their dashboard using the “Create new project” button in Figure 1 .

Figure 1: Dashboard for OSF

I have created a new project called ‘D-Lab Blog Post’ to demonstrate how to create a new registration. Once a project is created, OSF takes us to the project home page in Figure 2 below. The home page allows the researcher to navigate across different tabs - such as adding contributors to the project, adding files associated with the project, and most importantly, creating new registrations. To create a new registration, we click on the ‘Registrations’ tab highlighted in Figure 3 .

Figure 2: Home page for a new OSF project

To start a new registration, click on the ‘New Registration’ button ( Figure 3 ), which opens a window with the different types of registrations one can create ( Figure 4 ). To choose the right type of registration, OSF provides a guide on the different types of registrations available on the platform. In this project, I choose the OSF Preregistration template.

Figure 3: OSF page to create a new registration

Figure 4: Pop-up window to choose registration type

Once a pre-registration has been created, the researcher has to fill out information related to their research that includes hypotheses, the research design, the sampling design for recruiting respondents, the variables that will be created and measured in the experiment, and the analysis plan for analyzing the data ( Figure 5 ). OSF provides a detailed guide for how to create registrations that is helpful for researchers who are creating registrations for the first time. 

Figure 5: New registration page on OSF

Pre-registering the Misinformation Study

My co-author and I pre-registered our study on peer-to-peer correction of misinformation, detailing the hypotheses we were interested in testing, the design of our experiment (the treatment and control groups), how we would choose respondents for our survey, and how we would analyze the data we collected through Qualtrics. One of the simplest tests of our study included comparing the average level of correction among respondents who received a social norm nudge of either acceptability of correction or responsibility to correct to respondents who received no social norm nudge. We pre-registered how we would conduct this comparison, including the statistical tests relevant and the hypotheses they corresponded to. 

Once we had the data, we conducted the pre-registered analysis and found that social norm nudges – either the acceptability of correction or the responsibility of correction – appeared to have no effect on the correction of misinformation. In one case, they decreased the correction of misinformation (Figure 6). Because we had pre-registered our experiment and this analysis, we report our results even though they provide no evidence for our theory, and in one case, they go against the theory we had proposed.

Figure 6: Main results from the misinformation study

We conducted other pre-registered analyses, such as analyzing what influences people to correct misinformation when they see it. Our proposed hypotheses based on existing research were that:

• Those who perceive a higher degree of harm from the spread of the misinformation will be more likely to correct it
• Those who perceive a higher degree of futility from the correction of misinformation will be less likely to correct it.
• Those who believe they have expertise in the topic the misinformation is about will be more likely to correct it.
• Those who believe they will experience higher social sanctioning for correcting misinformation will be less likely to correct it.

We found support for all of these hypotheses, regardless of whether the misinformation was political or non-political (Figure 7). 

Figure 7: Results for when people correct and don’t correct misinformation

Exploratory Analysis of Misinformation Data

Once we had our data, we presented our results to different audiences, who suggested conducting different analyses to analyze it. Moreover, once we started digging in, we found interesting trends in our data as well! However, since we did not pre-register these analyses, we include them in our forthcoming paper only in the appendix under exploratory analysis. The transparency associated with flagging certain analyses as exploratory because they were not pre-registered allows readers to interpret results with caution.

Even though we did not pre-register some of our analysis, conducting it as “exploratory” gave us the opportunity to analyze our data with different methodologies – such as generalized random forests (a machine learning algorithm) and regression analyses, which are standard for political science research. The use of machine learning techniques led us to discover that the treatment effects of social norm nudges may be different for certain subgroups of people. Variables for respondent age, gender, left-leaning political ideology, number of children, and employment status turned out to be important for what political scientists call “heterogeneous treatment effects.” What this meant, for instance, is that women may respond differently to the social norm nudges than men. Though we did not explore heterogeneous treatment effects in our analysis, this exploratory finding from a generalized random forest provides an avenue for future researchers to explore in their surveys. 

Pre-registration of experimental analysis has slowly become the norm among political scientists. Top journals will publish replication materials along with papers to further encourage transparency in the discipline. Pre-registration can be an immensely helpful tool in the early stages of research, allowing researchers to think critically about their research questions and designs. It holds them accountable to conducting their research honestly and encourages the discipline at large to move away from only publishing results that are statistically significant and therefore, expanding what we can learn from experimental research.

Kamya Yadav

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

About half of americans say public k-12 education is going in the wrong direction.

About half of U.S. adults (51%) say the country’s public K-12 education system is generally going in the wrong direction. A far smaller share (16%) say it’s going in the right direction, and about a third (32%) are not sure, according to a Pew Research Center survey conducted in November 2023.

Pew Research Center conducted this analysis to understand how Americans view the K-12 public education system. We surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023.

The survey was conducted by Ipsos for Pew Research Center on the Ipsos KnowledgePanel Omnibus. The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted by gender, age, race, ethnicity, education, income and other categories.

Here are the questions used for this analysis , along with responses, and the survey methodology .

A majority of those who say it’s headed in the wrong direction say a major reason is that schools are not spending enough time on core academic subjects.

These findings come amid debates about what is taught in schools , as well as concerns about school budget cuts and students falling behind academically.

Related: Race and LGBTQ Issues in K-12 Schools

Republicans are more likely than Democrats to say the public K-12 education system is going in the wrong direction. About two-thirds of Republicans and Republican-leaning independents (65%) say this, compared with 40% of Democrats and Democratic leaners. In turn, 23% of Democrats and 10% of Republicans say it’s headed in the right direction.

Among Republicans, conservatives are the most likely to say public education is headed in the wrong direction: 75% say this, compared with 52% of moderate or liberal Republicans. There are no significant differences among Democrats by ideology.

Similar shares of K-12 parents and adults who don’t have a child in K-12 schools say the system is going in the wrong direction.

A separate Center survey of public K-12 teachers found that 82% think the overall state of public K-12 education has gotten worse in the past five years. And many teachers are pessimistic about the future.

Related: What’s It Like To Be A Teacher in America Today?

Why do Americans think public K-12 education is going in the wrong direction?

We asked adults who say the public education system is going in the wrong direction why that might be. About half or more say the following are major reasons:

• Schools not spending enough time on core academic subjects, like reading, math, science and social studies (69%)
• Teachers bringing their personal political and social views into the classroom (54%)
• Schools not having the funding and resources they need (52%)

About a quarter (26%) say a major reason is that parents have too much influence in decisions about what schools are teaching.

How views vary by party

Americans in each party point to different reasons why public education is headed in the wrong direction.

Republicans are more likely than Democrats to say major reasons are:

• A lack of focus on core academic subjects (79% vs. 55%)
• Teachers bringing their personal views into the classroom (76% vs. 23%)

In turn, Democrats are more likely than Republicans to point to:

• Insufficient school funding and resources (78% vs. 33%)
• Parents having too much say in what schools are teaching (46% vs. 13%)

Views also vary within each party by ideology.

Among Republicans, conservatives are particularly likely to cite a lack of focus on core academic subjects and teachers bringing their personal views into the classroom.

Among Democrats, liberals are especially likely to cite schools lacking resources and parents having too much say in the curriculum.

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Quantum breakthrough when light makes materials magnetic

The potential of quantum technology is huge but is today largely limited to the extremely cold environments of laboratories. Now, researchers at Stockholm University, at the Nordic Institute for Theoretical Physics and at the Ca' Foscari University of Venice have succeeded in demonstrating for the very first time how laser light can induce quantum behavior at room temperature -- and make non-magnetic materials magnetic. The breakthrough is expected to pave the way for faster and more energy-efficient computers, information transfer and data storage.

Within a few decades, the advancement of quantum technology is expected to revolutionize several of society's most important areas and pave the way for completely new technological possibilities in communication and energy. Of particular interest for researchers in the field are the peculiar and bizarre properties of quantum particles -- which deviate completely from the laws of classical physics and can make materials magnetic or superconducting. By increasing the understanding of exactly how and why this type of quantum states arise, the goal is to be able to control and manipulate materials to obtain quantum mechanical properties.

So far, researchers have only been able to induce quantum behaviors, such as magnetism and superconductivity, at extremely cold temperatures. Therefore, the potential of quantum research is still limited to laboratory environments.

Now, a research team from Stockholm University and the Nordic Institute of Theoretical Physics (NORDITA)* in Sweden, the University of Connecticut and the SLAC National Accelerator Laboratory in USA, the National Institute for Materials Science in Tsukuba, Japan, the Elettra-Sincrotrone Trieste, the 'Sapienza' University of Rome and the Ca' Foscari University of Venice in Italy, is the first in the world to demonstrate in an experiment how laser light can induce magnetism in a non-magnetic material at room temperature. In the study, published in Nature , the researchers subjected the quantum material strontium titanate to short but intense laser beams of a peculiar wavelength and polarization, to induced magnetism.

"The innovation in this method lies in the concept of letting light move atoms and electrons in this material in circular motion, so to generate currents that make it as magnetic as a refrigerator magnet. We have been able to do so by developing a new light source in the far-infrared with a polarization which has a "corkscrew" shape. This is the first time we have been able to induce and clearly see how the material becomes magnetic at room temperature in an experiment. Furthermore, our approach allows to make magnetic materials out of many insulators, when magnets are typically made of metals. In the long run, this opens for completely new applications in society," says the research leader Stefano Bonetti at Stockholm University and at the Ca' Foscari University of Venice

The method is based on the theory of "dynamic multiferroicity," which predicts that when titanium atoms are "stirred up" with circularly polarized light in an oxide based on titanium and strontium, a magnetic field will be formed. But it is only now that the theory can be confirmed in practice. The breakthrough is expected to have broad applications in several information technologies.

"This opens up for ultra-fast magnetic switches that can be used for faster information transfer and considerably better data storage, and for computers that are significantly faster and more energy-efficient," says Alexander Balatsky, professor of physics at NORDITA.

In fact, the results of the team have already been reproduced in several other labs, and a publication in the same issue of Nature demonstrates that this approach can be used to write, and hence store, magnetic information. A new chapter in designing new materials using light has been opened.

• Spintronics
• Quantum Physics
• Spintronics Research
• Quantum Computers
• Computers and Internet
• Quantum computer
• Quantum entanglement
• Quantum tunnelling
• Nanoparticle
• Absolute zero
• Quantum number
• Radiant energy
• Electron configuration

Story Source:

Materials provided by Stockholm University . Note: Content may be edited for style and length.

Journal Reference :

• M. Basini, M. Pancaldi, B. Wehinger, M. Udina, V. Unikandanunni, T. Tadano, M. C. Hoffmann, A. V. Balatsky, S. Bonetti. Terahertz electric-field-driven dynamical multiferroicity in SrTiO3 . Nature , 2024; DOI: 10.1038/s41586-024-07175-9

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