science projects quora

I haven’t heard the expression being used really, but here is my take on what it means: Data scientists build predictive models. That’s the core of what they do. In addition, they need to know a little bit of: Data engineering Software engineering Business analysis A full-stack data scientist would be able to seamlessly perform the role of a data engineer, software engineer, business analyst and data scientist. If you needed someone to develop an app, the FSDS could step in and do it. If you needed someone to set up a data warehouse, or to analyze the strategic management processes of a business, the FSDS could do it.

2. Mike West , SQL Server and Machine Learning enthusiast - 127,776 views, 45 answers

Excerpt from answer to: Is Python still relevant in data science given the rise of Scala (+Spark)?

Scala and Spark aren’t Python rivalries they are friends. I’ve been saying this for sometime now. Python is and will be the gold standard for machine learning over the next ten years. The only Python competitor is R and I’ll be honest, in the real world everyone’s using Python. You’ll see a lot of R at the college level but not in the applied space. Python simply has too much of a head start. Big Data is simply about getting any data (almost always unstructured data) into a format that can be modeled. Scala and Spark are just tools you can use to do that on very large data sets. TensorFlow wasn’t written in Scala. Don’t get caught up in one or two articles even if they are written by Andrew Ng. Do you own research.

3. Corrin Lakeland - 117,841 views, 87 answers

Excerpt from answer to: What will data scientists be working on in 5 to 10 years from now?

This brings me to the future. Over the next five years I expect to see lots of companies that are currently claiming to be involved actually trying using it on serious projects. I expect a good chunk of those projects to fail and the whole industry to have generally matured with far more understanding of what works and what doesn’t. Look at the number of GUI tools that support machine learning now. Things like Excel add-ons that automatically cluster data. Give it five years and I expect most people to think only of them when they think about data science. In ten years I think fashion will have well and truly moved on. Data Science will be a skill that is common and expected in other disciplines and specialist data scientists will be looked at a little strangely. You will also have a situation where is is common and normal for the data that is captured by systems to be amenable to data science, as opposed to what is happening now where most data is structured in a way that requires significant manipulation.

4. William Chen , Data Scientist at Quora - 117,834 views, 195 answers

Excerpt from answer to: Why did you choose to work in data science over quantitative finance?

The summary of all of the reasons I’m about to list was that I chose data science since I was more passionate about it . Here are 5 of the more specific reasons that led to my passion for data science. Excitement over a new, emerging, and growing career path - This decision was made sometime 2013 and 2014, when data science was even more new and uncertain than it was today. The idea of entering something where things were still developing and new appealed to me, and still does today. I try not to base my decisions based on hype - so this bullet is more about how the data science field was growing and would have a place for me rather than how it was hot. Familiarity towards data science - This is arguably the weakest reason on the list, but by the time that I had to choose what I would work on full-time, I had already had two data-science-related internships under my belt: one at Etsy (company) and one at Quora (company). I had great experiences at both of those internships, so choosing to work in data science full-time was a happy known quantity for me. Interest in working on a consumer internet product - I’ve had a longtime fascination with consumer internet products and have basically been excited to watch this whole space grow ever since I got access to dial-up. Working in data science was a unique opportunity for me to become a part of the consumer internet world I’ve been so fascinated by. Intrigue of working on a product thats new and upcoming - Consumer internet products were always interesting to me since they live in the land of uncertainty and could potentially become really big (or just fail). The intrigue of working on a product that could potentially become really important and knowing that you had a small role in it was tempting. Commitment towards knowledge-sharing - I’ve always been committed to sharing thoughts and ideas, either through being a teaching fellow for Harvard Stat 110 or writing as much as I can on Quora. Tech in general has a culture of meetups and blog posts and Quora answers and panels and invited talks. The same is not true in the secretive world of quantitative finance.

5. Clayton Bingham , Researcher in Center for Neural Engineering at University of Southern California - 108,512 views, 8 answers

Excerpt from answer to: In Python, how can I save data from a website to CSV using BeautifulSoup?

The lazy way would be to do something like this: Once you have your data in the dataframe you can do whatever parsing/reformatting you want. Or, if you only need this once you can just do that with Excel or something. I hope this helps!

6. Lili Jiang , Quora Data Science Manager - 88,461 views, 8 answers

Excerpt from answer to: As a data scientist, what tips would you have for a younger version of yourself?

First and foremost, is data science what you think it is? 9 out of 10 aspiring data scientists I come across equate machine learning with data science. “Data Science” is a loaded, catch-all term. Machine learning is a part of it, but at many major tech companies, product analytics is also an integral part of the data science team . Product analytics is a hidden gem. It is fun but doesn’t get talked about nearly as much. This includes: A/B test design Design metrics: Let’s take a video platform as an example. What is the best metric to optimize for that best represents user satisfaction? Should it be number of videos watched? Time spent watching videos? Percent of users that come back in a week to watch another video? Investigate why metrics change: Why is there suddenly a spike in activity in this cohort of users? Understand product mechanics: How do button X and feature Y improve the product? Should we redirect page A to B to C or go from A straight to C? Identify trends and offer strategic suggestions: Argue with data that the company should invest in ______ area to stay competitive in the field.

7. Zeeshan Zia , PhD in Computer Vision and Machine Learning - 70,564 views, 24 answers

Excerpt from answer to: Is AI over-hyped in 2017?

Yes and no, depending upon which community you are talking about. If you are talking about the academic research community, its not over-hyped. There have been major breakthroughs in AI over the past couple of years, and the celebration is certainly justified. In my own area of object recognition, we went from ~35% accuracy (mean average precision on Pascal VOC) to above 65% in just 3–4 years. Previously, we were advancing by 1–2% per year, despite object recognition being the hottest area of computer vision with the largest fraction of papers appearing in top conferences every year. Deep learning also made major breakthroughs in reinforcement learning, which is what yielded successes in general Atari game playing, and beat world grand master in Go decades ahead of expectations! It has finally enabled speech recognition to achieve useable levels of accuracy.

8. Jason T Widjaja , Business and analytics geek. Likes his brother. - 60,837 views, 167 answers

Excerpt from answer to: What is the risk of the hype around analytics/data science dying off, leaving lots of unemployed analysts?

Fundamentally, I do not see data science dying off anytime soon. As long as: people want to make better decisions (always), people care about what the future holds (forever), people and companies who do it well benefit (always) data points available continue to increase (forever), the tools and techniques we have continue to improve (you get the idea).. ...Analytics and data science is not going everywhere. Disclaimer: extremely biased sample size of one.

9. Roman Trusov , Master's Information Technology & Data Science, Skolkovo Institute of Science and Technology (2018) - 57,815 views, 139 answers

Excerpt from answer to: How should a data scientist handle versioning, both for pipeline code and models?

To get the best from version control system it’s better to separate them. Keeping code in version control system just like any other code is the only logical way, because if you, as a DS, perform some heavy ETL or if your code makes decisions that can bring/cost a lot of money, there’s no way it’s going around code review. No. Way. For some things that are more typical for data scientists, though, I don’t think that storing Jupyter notebooks in version control is a good practice. You can’t see a decent diff on them, they are not “production code” and in general, when you are finished with something, you want to push at least a “camera-ready” python script. Jupyter notebooks are great for experiments and demonstrations, but outside of these cases there’s always something better.

10. Shweta Doshi , Co-Founder GreyAtom,Data Science Immersive Learning school - 50,866 views, 123 answers

Excerpt from answer to: What is the essential knowledge and skills are required to start working as data scientist?

Essential knowledge you need to get yourself acquainted with falls under 3 categories i.e Programming, Maths and Science. As a data scientist you will be expected to take a business problem and translate it to a data question, create predictive models to answer the question and storytell about the findings. Statisticians that focus on implementing statistical approaches to data, and data managers who focus on running data science teams tend to fall in the data scientist role. Data scientists are the bridge between the programming and implementation of data science, the theory of data science, and the business implications of data.

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Introduction

I’m a curious person by nature. Whenever I come across a concept I haven’t heard of before, I can’t wait to dig in and find out how it works. This has come in quite handy in my own data science journey.

But before I landed my first break in data science, I was always curious about what data scientists actually did every day. Was I supposed to simply build models all the time? Or was the oft-quoted saying about spending 70-80% of our time cleaning data actually true?

I’m sure you have asked (or at least wondered) about this too. The role of a data scientist might be the “sexiest job of the 21st century”, but what does that entail on a day-to-day basis?

I decided to research this. I wanted to expand my horizons and understand how data scientists look at their role in different domains (such as NLP). This helped me gain a broader understanding of our role and why we should always read different perspectives when it comes to data science.

So, here is a list of the top 5 answers to help you get a sense of what the typical routine of a data scientist is. Prepare to be surprised – building models isn’t the primary (and only) function in a data scientist’s day-to-day tasks!

I also encourage you to take part in a discussion on this question here . This will enrich your current understanding of what a data scientist does and your thoughts will foster a discussion among our community!

Note: I have taken the answers verbatim from Quora and added my thoughts right at the beginning of each answer. This will help you get a good perspective of what the answer covers without diluting the author’s thoughts. Enjoy!

Machine Learning is Very Process Oriented – Mike West

I like this answer because it’s crisp, to-the-point and simple. The author has even designed a flow diagram and explained his thought process in a wonderfully illustrated way. Here is his answer in full:

Machine learning engineers spend a ton of time in the first two pictures (or stages). The fun part is really in the third stage but it’s only a small part of what happens in the real world.

Some key things to keep in mind about data science in the real world:

A Percentage-wise Breakdown of a Data Scientists’ Day-to-Day Role – Vinita Silaparasetty

I really like the use of visualization by Vinita. The percentage-wise description of each data science task is helpful and insightful. Vinita has also leaned on her experience to explain the step-by-step work a data scientist does. It’s a must-read answer!

There is a lot of backtracking involved. Sometimes you even need to be able to predict what consequences removing/adding a variable might have.

Data Scientist Perspective from a Small-Sized Company – Justin Fister

This is a superb answer and one I can relate to. Note that machine learning, the most anticipated aspect of a data scientist’s job, only occupies 5% of the total time! Just like Vinita, he has also explained his tasks in terms of percentage. Here is Justin’s view:

The “Data Scientist” is a bit of a Myth – Tim Kiely

The author, Tim Kiely, uses a Venn diagram to explain what data science is. Just take a look at this Venn diagram below – it will blow your mind. Tim additionally talks about what data scientists are supposed to be by taking a somewhat contradictory view of the general definition. Here is Tim’s answer:

The “Data Scientist” is a bit of a myth, in my opinion. Not to say they aren’t out there but they are far rarer than is popularly understood and are more of the exception than the rule.

I liken it to the “Web Master” title of the dot-com bubble – these supposed people who could do full stack programming, front end development, marketing, everything. All of those roles/skills were always specialized and remain so today.

“Data Scientists” are supposed to be database architects, understand distributed computing, have a deep understanding of statistics AND some area of business or field expertise. That’s asking a lot when any one of those skill sets can take a career to build.

The Data Scientists I’ve worked with typically have a Ph.D. in A.I. or Machine learning and are effective communicators, which gives them the ability to direct the analysts, DevOps people, programmers and DBA’s at their disposal to solve problems with data-driven solutions. They outline the desired solution and leave it to their teams to fill in the gaps.

Machine Learning Engineer Working on NLP Tasks – Evan Pete Walsh

Let’s drill down into a particular specialization of machine learning. One of my favorites – Natural Language Processing (NLP) ! I wanted to bring out a machine learning engineer’s view here (a role every data scientist should become familiar with). Check out Evan’s full response:

Currently working on NLP, for the most part, including intent classification and entity extraction. Here’s a typical day for me:

The data scientist role is truly multi-faceted, isn’t it? A LOT of aspiring data scientists assume that they will primarily be building models all day long but that simply isn’t the case.

There are all sorts of tasks involved in a typical data science project which you’ll find yourself working on day-to-day. I quite like that because it opens up avenues to learn new concepts and apply them in the real world.

I’ll be posting some more career-related articles on Analytics Vidhya, so stay tuned and keep learning!

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JavaScript in Plain English

Nov 27, 2020

My Computer Science Answer Went Viral On Quora

What are some tips for first-year (freshman year) computer science students.

Three years ago, I used to write very actively on Quora. I had just graduated from University and started working at a job that let’s say was not my dream job. I reflected on four years of journey of my graduation and realized things that I could have done differently and which could have changed the course of my career (which, by the way, was able to change later). I had my experience and my new found wisdom that I wanted to share with other people who were starting their graduation. By that time, I had already answered a few random questions and, at the same time, I was reading a lot on Quora. So, one day I was feeding myself with my daily dose of information on Quora and landed on this question.

What are some tips for first-year BTech CSE students?

I immediately started answering the question and poured my whole wisdom in the answer. I provided tips just like an elder brother would share with his younger sibling or, just like one senior would give it to his junior. I knew I was giving the best knowledge I had at that time and ended up spending more than an hour on it. After making sure, I wrote everything I wanted to write, I pressed the “Submit” button and the next thing I know, the answer was read by five thousand people in a single night. I was surprised to see that I was able to connect with so many people. The response has about 17 thousand views, which I know may not sound like a huge number, but I wasn’t even expecting that because earlier my answers used to have a few hundred views at the maximum. So, now that I have picked back up my long lost passion for writing again, I decided to share that answer here and will also try to curry the additional three years of learning at the end.

Here is the answer that I wrote:

Computer Science is a great branch and is quite interesting too if one is passionate about it. You can be a pro in a year or two if you work right from the first year and every day. So, here are a few tips which you can follow to excel in your career: Coding is what makes you different from others. You should read, write, learn, practice coding. Try to find out a new solution in your way. There always exist more than one solution to a problem. There are a number of coding websites available e.g. leetcode, codechef where you can practice and learn a lot. The good thing with CSE is that you can be at par or even ahead of those already in the industry if you work hard but in other branches until or unless you get into the industry you don’t get much practical knowledge. There are a number of online learning sites like COURSERA, EDX and lot of others where you can do an online course on almost every aspect of CSE. Browse those websites and find out what interests you and enrol in those courses and religiously attend courses on time. The problem is it is easy to enrol but difficult to be consistent. So be regular while you attend those courses. Try to find internships immediately after the second year to get practical exposure. Participate in coding events. Don’t resist yourself that you are a starter and don’t know enough. At least participate and you will realize many like you who have the same skill level as you. To motivate yourself to work more join freelancing websites and work on projects and earn. You can search on internet or quora to find more about freelancing. Start your blog to implement what you have learnt. Last but not the least maintain your GPA to average at least.

When I looked at these points again, I realised while a lot of them are still valid today, these tips are more focussed to prepare one for the job market. So, I will try to add perspective to not only prepare yourself for the industry but also to keep things in mind that can shape your career in the long run.

Understand the different career paths

Graduation in computer science comes with an overwhelming number of career options. It is essential to understand different career paths to make the best out of your time. The best way would be to research the different paths one can choose and try experimenting with different options to find out what interests you. The more involved you are in hackathons, conferences, projects and courses, the more you are going to discover what suits you.

Get involved with the tech community

The tech community is very active and continuously contributing towards lifting the fellow developers. To make the best out of college, try to get involved in the community as early as possible. You can get involved in many ways like helping others, writing articles, contributing in open source projects, answering questions on stack overflow. The more involved you are, the more you learn.

Stay up to date with the tech industry

The software industry is ever-changing and growing so much so that learning should never stop. There are newer and better technologies coming every day and, it is essential to stay up to date with them if you want to excel in this field. I try to stay updated by reading articles on Medium and Dev.io . You can read tech blogs from different tech companies to understand what are the challenges they face and how they tackle them.

I hope this article will help you get motivated to learn and make the best out of time. Also, college time is the best time, so try to enjoy as much as you can.

Happy learning!

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Towards Data Science

Jun 8, 2017

Identifying duplicate questions on Quora | Top 12% on Kaggle!

If you are a regular Quoran like me, you have most likely stumbled on duplicate questions asking the same essential question.

This is a bad user experience for both writers and seekers, as the answers get fragmented across different versions of the same question. In fact, this is a problem which is very evident on other social Q&A platforms like StackOverflow. A real-world problem out there, begging AI to solve it :)

For the past month or so i’ve spent my nights cracking my skull open at this problem, along with three thousand other kagglers! It was my second serious competition in a row, it was stressful at times, but i learnt a lot overall — Landed a Top 12% position, a discussion gold medal and a couple of kernel bronze medals :)

A bout the problem — Quora has given an (almost) real-world dataset of question pairs, with the label of is_duplicate along with every question pair. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. There were around 400K question pairs in the training set while the testing set contained around 2.5 million pairs. Yeah, 2.5 million! A large majority of those pairs were computer-generated questions to prevent cheating, but 2 and a half million, god! I was maxing out my poor 8GB machine every other hour :(

My approach — I started off with the xgboost starter by @anokas, and built upon it gradually. My feature-set involved around 70 features which is on a rather lower range, when compared to the Top Kagglers’ approaches. My features could be broadly classified into NLP-based features, word `embedding based distances and graph-based features. Let me elaborate:

N LP Features : Some very obvious text-based features are percentage of words matching between the two questions, length of the two questions, number of words, number of sentences, number of stopwords, the usual natural language stuff! I tried going ahead with tfidf scores, but that was both not very useful and computationally expensive. Instead, one of the most important features turned out to be weighted word match share, where each word’s weight is the inverse frequency of the word in the corpus(basically idf) — if i have a rare word in both the questions, they might be discussing similar topics. I also had features like cosine distance, jaccard distance, jarowinkler distance, hamming distance, and n-gram matches(shingling).

A library i used extensively for NLP tasks was Spacy, which has been developing some great functionalities lately — spacy similarity turned out to be a good feature too. Some creative ones i thought of were related to the kind of question — whether it is a “How” question or “Why” question — dependent on the first word of the sentence. Strangely, when modelling this i should’ve thought of building “last word similarity” as well, completely missed that! Named entities are a crucial key to understand the context of a question — thus common_named_entity score and common_noun_chunk score were an obvious choice. I pushed out a kernel about calculating similarity via the wordnet corpus, however wordnet fell short of spacy in terms of ease of use, speed and vocabulary size.

Word Embeddings based Distances :

When doing a NLP competition, can Word2Vec be left behind! I feel like word2vec might be the coolest computer science concept i’ve read, i always get blown away by its effectiveness. Every.Single.Time.

Anyway, i had the questions mapped to 300-dimensional vectors in a Sent2Vec format, resulting in a vector for every question. Naturally, distance-based features between vectors were built — cosine, cityblock, jacard, canberra, euclidean and braycurtis. Must mention @abhishek’s scripts for the inspiration for these features. Unfortunately, i couldn’t build the real embedding layers which i could pass in to the lstm layers in keras — my RAM just wouldn’t let me. I’m getting some serious hardware, soon!

G raph Features : In an NLP competition, these graph features played quite the spoilsport! However, it was a nice reminder of how the theories of social networks might apply in datasets like Quora’s question pairs.

Here, every question is a node in the graph and a question pair in the dataset indicates an edge between the two nodes. We can use the graph structure of the test data as well, since we’re not considering the is_duplicate label anywhere — Those 2 million edges contributed a whole lot to the graph! There were heated discussions between kagglers on whether the graph-based features are supposedly “magic features”, which should be released to level the playing field. Anyway, all of these features gave significant boosts to most models:

Transitivity Magic — Continuing on the graph structure, modelling transitivity between the questions was an obvious approach. For eg. if Q1 is similar to Q2 and Q2 is similar to Q3, it implies Q1 is similar to Q3 more often than not(as per our dataset). This was one of the features i had begun building but left it midway, a costly mistake. Many top solutions used this feature in some form or another, a simpler version was to average probabilities of duplicacy between every question and its counterpart’s neighbors.

My Whiteboarding Sessions

Some fuckups.

When a noob like me dives head-first into kaggling big-time, there are bound to be fuckups. Even though i make a conscious effort to keep my pipeline modular and version-controlled, i lost almost a weekend’s worth of work because of a nasty bug in my pipelining. Learning it the hard way, i git reverted and remodelled the pipeline on an ipython notebook . A major challenge that i faced when building features was writing memory-efficient code and not rebuilding previous features, modularity was key. This led to another fuckup — since i couldn’t handle the whole of my testing dataset in my RAM, i was breaking it into six subsets and building features iteratively. This meant i was doing a pandas concat between the older dataframe with the new feature, little did i concentrate on indexing :( Spent more than a couple days scratching my head at all NaN features. Noob mistake.

The QID Conundrum:

The training dataset had an ID for every question, thus a QID1 and QID2 in every row. However, this wasn’t the case with the testing dataset which meant “QID” couldn’t be used as a feature straightaway. A fellow kaggler released an incredibly creative observation of the decreasing average duplicate ratio(rolling mean) with increasing QID — Mostprobably an indication of Quora’s improving algorithm with time, thus reducing the number of duplicate questions with increasing ID. This inference is based on the assumption that the QID values aren’t masked and are truly representative of time of posting the question. To model QIDs in our testing dataset, i had a hashtable which mapped question texts to QIDs. Now iterating over all questions in test_df — If i encountered an existent question, the corresponding QID was assigned to it. Else we assumed that this was a new question in order of time of posting, incrementing QID by 1. This led to various features like QID difference, mean QID, min QID with the hope of modelling the decrease of duplicacy over time.

Class Imbalance:

A major part of discussions were centered around guesstimating the class split in the testing dataframe, which wasn’t clearly similar to the testing split. The mathy folks figured a narrow range of the split via a couple of constant valued submissions, here and here . There were around 34% positive duplicates in the training set, while the testing set had an estimated 16%-17% positive duplicates — Probably a result of the improved Quora algorithm or a result of the computer-generated question pairs. Anyway, a misrepresentative training dataset wasn’t going to help matters — people came up with oversampling solutions(duplicating the negative rows in training), or rescaled their predictions by an appropriate factor.

My models — XGBoost is love, XGBoost is life

Very embarassed to admit, but my submission was just a single model solution, a 2000-round xgboost. To be fair, i didn’t spend a lot of time on parameter tuning or building diverse models, left it for too late. I tried the default random forest and GBM, not to a better effect than XGB. Stacking and ensembling were in the roadmap too, but that just stayed there :(

Instead, i spent a very long weekend at learning Keras and building dense neural nets — ’twas my first time! Went through understanding various hyperparameters and ideal architectures, the theory behind activation functions, dropout layers and optimizers! Very interesting stuff! I played around with my a two-layer sigmoid neural net with my 70 features tuning it to perfection, it never came close to the xgboost though. As i mentioned earlier, i couldn’t build embedding layers or LSTMs with my hardware, that would’ve definitely helped. Soon.

Proud of my modelling pipeline though, didn’t have to fret over making a function for every model– will be helpful when i build hundreds of models to stack. Soon.

Conclusions :

I should manage my time better, assigning appropriate time for exploration, feature engineering, model building and stacking. Really felt the crunch in the last week as i ran short of submissions.

So yeah, that’s that. One competition ends, another begins. Kaggle is addictive!

This was originally posted on my blog . One Blog Every Week

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Machine Learning Engineer. Quora. May 2022 ; Software Engineer. athenahealth. Aug 2021 ; Machine Learning Course Assistant. New York University. Sep 2020 ; Project
My Computer Science Answer Went Viral On Quora
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Identifying duplicate questions on Quora
About the problem — Quora has given an (almost) real-world dataset of question pairs, ... 3 Data Science Projects That Got Me 12 Interviews.