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Hiring CS Graduates: What We Learned from Employers

Computer science ( CS ) majors are in high demand and account for a large part of national computer and information technology job market applicants. Employment in this sector is projected to grow 12% between 2018 and 2028, which is faster than the average of all other occupations. Published data are available on traditional non-computer science-specific hiring processes. However, the hiring process for CS majors may be different. It is critical to have up-to-date information on questions such as “what positions are in high demand for CS majors?,” “what is a typical hiring process?,” and “what do employers say they look for when hiring CS graduates?” This article discusses the analysis of a survey of 218 recruiters hiring CS graduates in the United States. We used Atlas.ti to analyze qualitative survey data and report the results on what positions are in the highest demand, the hiring process, and the resume review process. Our study revealed that a software developer was the most common job the recruiters were looking to fill. We found that the hiring process steps for CS graduates are generally aligned with traditional hiring steps, with an additional emphasis on technical and coding tests. Recruiters reported that their hiring choices were based on reviewing resume’s experience, GPA, and projects sections. The results provide insights into the hiring process, decision making, resume analysis, and some discrepancies between current undergraduate CS program outcomes and employers’ expectations.

A Systematic Literature Review of Empiricism and Norms of Reporting in Computing Education Research Literature

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.

Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.

A genre-based analysis of questions and comments in Q&A sessions after conference paper presentations in computer science

Gender diversity in computer science at a large public r1 research university: reporting on a self-study.

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.

Designing for Student-Directedness: How K–12 Teachers Utilize Peers to Support Projects

Student-directed projects—projects in which students have individual control over what they create and how to create it—are a promising practice for supporting the development of conceptual understanding and personal interest in K–12 computer science classrooms. In this article, we explore a central (and perhaps counterintuitive) design principle identified by a group of K–12 computer science teachers who support student-directed projects in their classrooms: in order for students to develop their own ideas and determine how to pursue them, students must have opportunities to engage with other students’ work. In this qualitative study, we investigated the instructional practices of 25 K–12 teachers using a series of in-depth, semi-structured interviews to develop understandings of how they used peer work to support student-directed projects in their classrooms. Teachers described supporting their students in navigating three stages of project development: generating ideas, pursuing ideas, and presenting ideas. For each of these three stages, teachers considered multiple factors to encourage engagement with peer work in their classrooms, including the quality and completeness of shared work and the modes of interaction with the work. We discuss how this pedagogical approach offers students new relationships to their own learning, to their peers, and to their teachers and communicates important messages to students about their own competence and agency, potentially contributing to aims within computer science for broadening participation.

Creativity in CS1: A Literature Review

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

CATS: Customizable Abstractive Topic-based Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

Exploring students’ and lecturers’ views on collaboration and cooperation in computer science courses - a qualitative analysis

Factors affecting student educational choices regarding oer material in computer science, export citation format, share document.

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Science Publications is pleased to announce the launch of a new open access journal, Journal of Adaptive Structures. JAS brings together emerging technologies for adaptive smart structures, including advanced materials, smart actuation, sensing and control, to pursue the progressive adoption of the major scientific achievements in this multidisciplinary field on-board of commercial aircraft. 

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2023 author index, 2 k -vertex kernels for cluster deletion and strong triadic closure, characterization of exact one-query quantum algorithms for partial boolean functions, hadamard encoding based frequent itemset mining under local differential privacy.

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Join the community, trending research, stream-k: work-centric parallel decomposition for dense matrix-matrix multiplication on the gpu.

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We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra.

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WorldCIST 2021: Trends and Applications in Information Systems and Technologies pp 13–22 Cite as

Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors

  • Phoey Lee Teh   ORCID: orcid.org/0000-0002-7787-1299 19 &
  • Peter Heard   ORCID: orcid.org/0000-0002-5135-7822 20  
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  • First Online: 29 March 2021

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1366)

This study reveals common factors among highly cited papers in the computer sciences. The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS). Data on the number of citations, number of authors, article length and subject sub-discipline were extracted and analyzed in order to identify trends, relationships and common features. Correlations between common factors were analyzed. The 500 papers were cited a total of 10,926 times: the average number of citations per paper was 21.82 citations. A correlation was found between author credibility (defined in terms of the QS University Ranking of the first named author’s affiliation) and the number of citations. Authors from universities ranked 350 or higher were more cited than those from lower ranked universities. Relationships were also found between journal ranking and both the number of authors and the article length. Higher ranked journals tend to have a greater number of authors, but were of shorter length. The article length was also found to be correlated with the number of authors and the QS Subject Ranking of the first author’s affiliation. The proportion of articles in higher ranked journals (journal quartile), the length of articles and the number of citations per page were all found to correlate to the sub-discipline area (Information Systems; Software Engineering; Artificial Intelligence; Interdisciplinary Applications; and Theory and Methods).

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Teh, P.L., Heard, P. (2021). Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_2

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  • ABI/INFORM ProQuest access to articles on business and management from U.S. and international journals and trade magazines, including company histories, competitive intelligence, and product development. Coverage: 1971 - present more... less... Includes scholarly journals, trade magazines, working papers, market research, and company profiles.
  • Factiva News, business magazines, trade journals, newsletters, and television and radio transcripts. Coverage: 1970s - present. Varies by publication more... less... Factiva forbids storing or using search results in research applications such as data mining or trend analysis. Note: If you are at a public workstation you will be asked for your WatIAM (Quest) User ID and Password.
  • Nexis Uni News, legal, and business sources from around the world, Find news, legislation, case law, statutes, company profiles, market & industry reports. Coverage: 1970s - present more... less... Note: If you are at a public workstation you will be asked for your WatIAM (Quest) User ID and Password.

Multidisciplinary research databases

Not sure where to start? The databases below cover many disciplines including math, business, economics, health, life science, physical science, and technology.

  • Scopus Peer-reviewed literature from scientific journals, books and conference proceedings, covering the fields of science, technology, medicine, social sciences, and arts and humanities. Coverage: 1966 - present
  • Web of Science Articles and citations in the sciences, social sciences, arts, and humanities. Coverage: Varies more... less... Web of Science is comprised of several databases. The Science Citation Index Expanded (SCI) covers journals in the medical, physical and natural sciences, and engineering fields. The entire database extends back to 1899. The Social Sciences Citation Index (SSCI) covers journals in the social sciences. The entire database extends back to 1898. The Arts & Humanities Citation Index (AHCI) covers journals in the arts and humanities. It also selectively covers relevant items from science and technical journals. The entire database extends back to 1975.
  • Google Scholar Google Scholar is a search engine finds scholarly information from many sources (however, not everything in Google Scholar is scholarly). To access materials paid for by your library, go to Google Scholar, then choose Settings and click "Library Links" to add the University of Waterloo. more... less... Google Scholar is a search engine that emphasizes scholarly information, particularly in the sciences and technology. It draws from academic publishers, professional societies, preprint repositories and universities. Note: Access To access materials paid for by your library, go to Google Scholar, then choose Settings and click "Library Links." The off-campus user will first need to login via "Get access from anywhere."
  • arXiv A pre-print server which hosts papers (that have not been peer reviewed) relating to physics, mathematics, computer science, nonlinear sciences, qualitative biology and statistics Coverage: 1991 - present
  • JSTOR Provides access to back issues of journals in the humanities, social sciences, and physical sciences, many of which date from the 1800s. Coverage: varies (excludes current 3 to 5 years)
  • ProQuest A platform with many databases of journal indexes and abstracts, as well as some with full text Coverage: Varies more... less... This online platform hosts multiple resources.
  • EBSCOhost A platform with many databases of journal indexes and abstracts, as well as some with full text Coverage: varies more... less... This online platform hosts multiple resources. Note: Offline digital lending: Requires Adobe Digital Editions.

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The computing and information revolution is transforming society. Cornell Computer Science is a leader in this transformation, producing cutting-edge research in many important areas. The excellence of Cornell faculty and students, and their drive to discover and collaborate, ensure our leadership will continue to grow.

The contributions of Cornell Computer Science to research and education are widely recognized, as shown by two Turing Awards, two Von Neumann medals, two MacArthur "genius" awards, and dozens of NSF Career awards our faculty have received, among numerous other signs of success and influence.

To explore current computer science research at Cornell, follow links at the left or below.

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Knowledge representation, machine learning, NLP and IR, reasoning, robotics, search, vision

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Statistical genetics, sequence analysis, structure analysis, genome assembly, protein classification, gene networks, molecular dynamics

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

IT & Computer Science Research Topics

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

  • Algorithms & data structures
  • Artificial intelligence ( AI )
  • Computer networking
  • Database systems
  • Human-computer interaction
  • Information security (IS)
  • Software engineering
  • Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

  • An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
  • A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
  • An evaluation of machine learning algorithms used for recommender systems in streaming services
  • A review of approximation algorithm approaches for solving NP-hard problems
  • An analysis of parallel algorithms for high-performance computing of genomic data
  • The influence of data structures on optimal algorithm design and performance in Fintech
  • A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
  • A comparison of streaming algorithm performance for the detection of elephant flows
  • A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
  • Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
  • Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
  • A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
  • A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
  • Exploring the feasibility of applying information theory to feature extraction in retail datasets.
  • Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

  • Applying deep learning algorithms for speech recognition in speech-impaired children
  • A review of the impact of artificial intelligence on decision-making processes in stock valuation
  • An evaluation of reinforcement learning algorithms used in the production of video games
  • An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
  • An analysis of the ethical and social implications of artificial intelligence-based automated marking
  • The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
  • An examination of the use of artificial intelligence in orthopaedic surgery
  • The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
  • An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
  • A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Research topic evaluator

Topics & Ideas: Networking

  • An analysis of the impact of 5G technology on internet penetration in rural Tanzania
  • Assessing the role of software-defined networking (SDN) in modern cloud-based computing
  • A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
  • Exploring the influence of cloud computing on security risks in fintech.
  • An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
  • Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
  • An evaluation of the challenges and opportunities in 6G wireless network adoption
  • The role of network congestion control algorithms in improving network performance on streaming platforms
  • An analysis of network coding-based approaches for data security
  • Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

  • An analysis of big data management systems and technologies used in B2B marketing
  • The impact of NoSQL databases on data management and analysis in smart cities
  • An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
  • Exploring the role of data warehousing and business intelligence in global consultancies
  • An analysis of the use of graph databases for data modelling and analysis in recommendation systems
  • The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
  • An examination of the challenges and opportunities of distributed databases in supply chain management
  • Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
  • An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
  • Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

  • An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
  • An exploration of how artificial intelligence is changing human-computer interaction patterns in children
  • An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
  • Assessing the influence of virtual and augmented reality on consumer purchasing patterns
  • An examination of the use of gesture-based interfaces in architecture
  • Exploring the impact of ease of use in wearable technology on geriatric user
  • Evaluating the ramifications of gamification in the Metaverse
  • A systematic review of user experience (UX) design advances associated with Augmented Reality
  • A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
  • Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

  • A bibliometric review of current trends in cryptography for secure communication
  • An analysis of secure multi-party computation protocols and their applications in cloud-based computing
  • An investigation of the security of blockchain technology in patient health record tracking
  • A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
  • A systematic review of secure data storage solutions used for cloud computing in the fintech industry
  • An analysis of intrusion detection and prevention systems used in the healthcare sector
  • Assessing security best practices for IoT devices in political offices
  • An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
  • A comparative study of digital signature schemes adoption in property transfers
  • An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

  • A study of agile software development methodologies and their impact on project success in pharmacology
  • Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
  • A study of the impact of DevOps practices on software development and delivery in the healthcare sector
  • An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
  • A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
  • An investigation of software testing techniques and methodologies for subscription-based offerings
  • A review of software security practices and techniques for protecting against phishing attacks from social media
  • An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
  • Exploring the impact of software development outsourcing on project success in multinational contexts
  • An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

  • An array-based optimization framework for query processing and data analytics (Chen, 2021)
  • Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
  • Embedding constructural documentation in unit tests (Nassif, 2019)
  • PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
  • Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
  • Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
  • Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
  • Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
  • Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
  • Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
  • Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
  • A state-based approach to context modeling and computing (Yue, 2019)
  • A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
  • HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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The top list of computer science research databases

The best research databases for computer science

1. ACM Digital Library

2. ieee xplore digital library, 3. dblp computer science bibliography, 4. springer lecture notes in computer science (lncs), frequently asked questions about computer science research databases, related articles.

Besides the interdisciplinary research databases Web of Science and Scopus there are also academic databases specifically dedicated to computer science. We have compiled a list of the top 4 research databases with a special focus on computer science to help you find research papers, scholarly articles, and conference papers fast.

ACM Digital Library is the clear number one when it comes to academic databases for computer science. The ACM Full-Text Collection currently has 540,000+ articles, while the ACM Guide to Computing Literature holds more than 2.8+ million bibliographic entries.

  • Coverage: 2.8+ million articles
  • Abstracts: ✔
  • Related articles: ✘
  • References: ✔
  • Cited by: ✔
  • Full text: ✔ (requires institutional subscription)
  • Export formats: BibTeX, EndNote

Search interface of the ACM Digital Library

IEEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.

  • Coverage: 4.7+ million articles
  • Export formats: BibTeX, RIS

Search interface of IEEE Xplore

Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. Its index covers journal articles, conference and workshop proceedings, as well as monographs.

  • Coverage: 4.3 million articles
  • Abstracts: ✘
  • References: ✘
  • Cited by: ✘
  • Full text: ✘ (Links to publisher websites available)
  • Export formats: RIS, BibTeX

Search interface of dbpl

Springer's Lecture Notes in Computer Science is the number one publishing source for conference proceedings covering all areas of computer science.

  • Coverage: 415,000+ articles
  • Export formats: RIS, EndNote, BibTeX

Search interface of Springer Lecture Notes in Computer Science

Hosted at the University of Trier, Germany, dbpl has become an indispensable resource in the field of computer science. It's index covers journal articles, conference and workshop proceedings, as well as monographs.

Microsoft Academic was a free academic search engine developed by Microsoft Research. It had more than 13.9 million articles indexed. It was shut down in 2022.

EEE Xplore holds more than 4.7 million research articles from the fields of electrical engineering, computer science, and electronics. It not only covers articles published in scholarly journals, but also conference papers, technical standards, as well as some books.

Content analysis illustration

Researchers Show Classical Computers Can Keep Up with, and Surpass, Their Quantum Counterparts

Researchers Adopt Innovative Method to Boost Speed and Accuracy of Traditional Computing

Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.

Many see quantum computing’s advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits  (0s and 1s), while quantum computers deploy quantum bits (qubits) to store quantum information in values between 0 and 1. Under certain conditions this ability to process and store information in qubits can be used to design quantum algorithms that drastically outperform their classical counterparts. Notably, quantum’s ability to store information in values between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum ones.

However, quantum computers are finicky and have a tendency to lose information. Moreover, even if information loss can be avoided, it is difficult to translate it into classical information—which is necessary to yield a useful computation.

Classical computers suffer from neither of those two problems. Moreover, cleverly devised classical algorithms can further exploit the twin challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought—as recently reported in a research paper in the journal PRX Quantum .

The scientists’ results show that classical computing can be reconfigured to perform faster and more accurate calculations than state-of-the-art quantum computers.  

This breakthrough was achieved with an algorithm that keeps only part of the information stored in the quantum state—and just enough to be able to accurately compute the final outcome.

“This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches,” explains Dries Sels, an assistant professor in New York University’s Department of Physics and one of the paper’s authors. “Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer.”

In seeking ways to optimize classical computing, Sels and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents the interactions between the qubits. Those types of networks have been notoriously hard to deal with, but recent advances in the field now allow these networks to be optimized with tools borrowed from statistical inference. 

The authors compare the work of the algorithm to the compression of an image into a JPEG file, which allows large images to be stored using less space by eliminating information with barely perceivable loss in the quality of the image.

“Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image,” says the Flatiron Institute’s Joseph Tindall, who led the project. “We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further.”

The work was supported by the Flatiron Institute and a grant from the Air Force Office of Scientific Research (FA9550-21-1-0236).

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ScienceDaily

Researchers show classical computers can keep up with, and surpass, their quantum counterparts

Researchers adopt innovative method to boost speed and accuracy of traditional computing.

Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.

Many see quantum computing's advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits (0s and 1s), while quantum computers deploy quantum bits (qubits) to store quantum information in values between 0 and 1. Under certain conditions this ability to process and store information in qubits can be used to design quantum algorithms that drastically outperform their classical counterparts. Notably, quantum's ability to store information in values between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum ones.

However, quantum computers are finicky and have a tendency to lose information. Moreover, even if information loss can be avoided, it is difficult to translate it into classical information -- which is necessary to yield a useful computation.

Classical computers suffer from neither of those two problems. Moreover, cleverly devised classical algorithms can further exploit the twin challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought -- as recently reported in a research paper in the journal PRX Quantum .

The scientists' results show that classical computing can be reconfigured to perform faster and more accurate calculations than state-of-the-art quantum computers.

This breakthrough was achieved with an algorithm that keeps only part of the information stored in the quantum state -- and just enough to be able to accurately compute the final outcome.

"This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches," explains Dries Sels, an assistant professor in New York University's Department of Physics and one of the paper's authors. "Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer."

In seeking ways to optimize classical computing, Sels and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents the interactions between the qubits. Those types of networks have been notoriously hard to deal with, but recent advances in the field now allow these networks to be optimized with tools borrowed from statistical inference.

The authors compare the work of the algorithm to the compression of an image into a JPEG file, which allows large images to be stored using less space by eliminating information with barely perceivable loss in the quality of the image.

"Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image," says the Flatiron Institute's Joseph Tindall, who led the project. "We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further."

The work was supported by the Flatiron Institute and a grant from the Air Force Office of Scientific Research (FA9550-21-1-0236).

  • Quantum Computers
  • Computers and Internet
  • Information Technology
  • Spintronics Research
  • Distributed Computing
  • Computer Science
  • Quantum computer
  • Quantum entanglement
  • Grid computing
  • Computing power everywhere
  • Quantum tunnelling
  • Introduction to quantum mechanics
  • Quantum mechanics

Story Source:

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

Journal Reference :

  • Joseph Tindall, Matthew Fishman, E. Miles Stoudenmire, Dries Sels. Efficient Tensor Network Simulation of IBM’s Eagle Kicked Ising Experiment . PRX Quantum , 2024; 5 (1) DOI: 10.1103/PRXQuantum.5.010308

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February 9, 2024

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Researchers show classical computers can keep up with, and surpass, their quantum counterparts

by James Devitt, New York University

Researchers show classical computers can keep up with, and surpass, their quantum counterparts

Quantum computing has been hailed as a technology that can outperform classical computing in both speed and memory usage, potentially opening the way to making predictions of physical phenomena not previously possible.

Many see quantum computing's advent as marking a paradigm shift from classical, or conventional, computing. Conventional computers process information in the form of digital bits (0s and 1s), while quantum computers deploy quantum bits (qubits) to store quantum information in values between 0 and 1.

Under certain conditions, this ability to process and store information in qubits can be used to design quantum algorithms that drastically outperform their classical counterparts. Notably, quantum's ability to store information in values between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum ones.

However, quantum computers are finicky and have a tendency to lose information. Moreover, even if information loss can be avoided, it is difficult to translate it into classical information—which is necessary to yield a useful computation.

Classical computers suffer from neither of those two problems. Moreover, cleverly devised classical algorithms can further exploit the twin challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought—as recently reported in a research paper in the journal PRX Quantum .

The scientists' results show that classical computing can be reconfigured to perform faster and more accurate calculations than state-of-the-art quantum computers.

This breakthrough was achieved with an algorithm that keeps only part of the information stored in the quantum state —and just enough to be able to accurately compute the final outcome.

"This work shows that there are many potential routes to improving computations, encompassing both classical and quantum approaches," explains Dries Sels, an assistant professor in New York University's Department of Physics and one of the paper's authors. "Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer."

In seeking ways to optimize classical computing, Sels and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents the interactions between the qubits. Those types of networks have been notoriously hard to deal with, but recent advances in the field now allow these networks to be optimized with tools borrowed from statistical inference.

The authors compare the work of the algorithm to the compression of an image into a JPEG file, which allows large images to be stored using less space by eliminating information with barely perceivable loss in the quality of the image.

"Choosing different structures for the tensor network corresponds to choosing different forms of compression, like different formats for your image," says the Flatiron Institute's Joseph Tindall, who led the project. "We are successfully developing tools for working with a wide range of different tensor networks. This work reflects that, and we are confident that we will soon be raising the bar for quantum computing even further."

Journal information: PRX Quantum

Provided by New York University

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  • Programming

Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

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Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

Top Computer Science Research Topics

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

  • Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
  • Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
  • OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

  • Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
  • Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
  • Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

  • Know your interest.
  • Choose the topic wisely.
  • Make proper research about the demand of the topic.
  • Get proper references.
  • Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

 There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

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What’s the difference between data science and computer science?

Students use an augmented reality device to study at library

Computer science interacts with data, and data science interacts with computers. Some schools have schools have distinct computer science and data science programs. Others combine them into one.

So, what gives?

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For someone not involved in the tech space in particular, it may be difficult to discern what the true differences are between the two closely related areas. And even for those in tech, sometimes there is a gray area as to what falls into each bucket.

The good news is that both data science and computer science are growing areas in terms of educational offerings and job opportunities. There is an increasing number of degrees, certifications, and bootcamps that are teaching the in-demand hard skills—like programming, machine learning , and data analytics —best catered to each area. Plus, tech salaries remain high; according to Dice, the average tech salary was $111,193 in 2023.  

But the question remains: what’s the true difference between data and computer science? This piece will provide some insight.

What is data science?

As the name implies, data science focuses deeply on the collection, organization, and the extraction of data within the context of real-world problems, often in business. Fortune has a comprehensive guide diving more specifically into the intricacies of data science. 

The study of data science often leads to careers as data scientists, data analysts, data engineerings, and more. Data-related occupations are growing faster than the national average for all U.S. jobs, with data scientists in particular growing at a rate of 35%, according to the U.S. Bureau of Labor Statistics . Plus, they have great salaries—averaging about $103,500 per year.

Data science skills center around programming languages like SQL, R, and Python as well as knowledge of statistics, mathematics, and AI.

Data science is a subdiscipline of computer science with offshoots in machine learning and big data statistics, says Jignesh Patel, professor at Carnegie Mellon University and co-founder of DataChat .

“Just as a doctor might specialize in pediatrics or surgery, a computer scientist might specialize in data science,” Patel says. 

What is computer science?

Computer science is an even broader term that focuses on the study of computers, including software, hardware, networks, and AI. 

“Careers in computer science cover a very broad range of functions such as software development, software engineering and architecture, computer security, development and management of databases,” says Mamdouh Refaat, chief data scientist at Altair .

From a degree standpoint, computer science programs offer many different concentrations and specializations available that may include data science, cybersecurity, machine learning, and AI. Resultantly, those who study computer science are not necessarily siloed into a computer scientist job title. Software developer, systems analyst, and computer engineer are just a few examples. 

Overall, demand for computer science-related roles are growing, and most of the time average salaries are near or within six-figures, based on data from the U.S. Bureau of Labor Statistics .

What is the difference between data and computer science?

The main difference between data and computer science is the level of focus. Computer science is a much more all-encompassing area of study—that even includes data science itself.

For computer science, the differentiating skills may include increased knowledge of computer networking, cybersecurity, and algorithms. Computer scientists may know a wide variety of programming languages like JavaScript, C++, HTML, and more. The subject also sometimes pairs well with other non-tech areas like physics and economics.

Data science, on the other hand, is much more of a niche subfield that intersects more closely with mathematics and statistics. The important skills include know-how of machine learning, AI, and deep learning. Plus the relevant programming languages are narrower and include SQL, R, and Python. 

It is less popular to find a “computer science” certification or bootcamp programs. Instead, they may be more focused on a subject like IT, data analytics , AI, cybersecurity, and more. 

“The two fields intersect in the fields of artificial intelligence, data management and programming,” Refaat tells Fortune. “However, computer science can be described as focused on the creation of programming environments and software in general, while data science is focused on exploring and using data.”

Since both fields are continuing to grow, Refaat adds that experts should expect a lifetime of learning—especially in a business problem-solving context.

“Employers in any industry seek data and computer scientists with deep understanding of their specific industry and its unique problems and areas of application,” he says. “Although basic skills in both fields are transferable between industries, having the business insight is a big advantage.”

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    Classical computers suffer from neither of those two problems. Moreover, cleverly devised classical algorithms can further exploit the twin challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought—as recently reported in a research paper in the journal PRX Quantum.

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    The work was supported by the Flatiron Institute and a grant from the Air Force Office of Scientific Research (FA9550-21-1-0236). RELATED TOPICS Computers & Math

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    Forming the edge environment from the norm network of a tensor network state. One of the edges e is split and all other indices of the network are contracted over, reducing the cut network to a ...

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