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Top 10 Quora Data Science Writers and Their Best Advice


Top Quora data science writers give their advice on pursuing a career in the field, approaching interviews, and selecting appropriate technologies.



Here is a list of top 10 Data Science writers on Quora and their selected answers.

Quora Data Science
1. William Chen, Data Scientist at Quora. 85098 views

Selected answer to: How Can I Become A Data Scientist?

Start by learning scikit-learn, playing around, reading through tutorials and forums at Data Science London + Scikit-learn for a simple, synthetic, binary classification task. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster with a slightly more complicated binary classification task (with categorical variables, missing values, etc.)


2. Sahil Sareen, Software Engineer at Arista Networks, Foundation Member and Game Dev at GNOME. 71198 views

Selected answer to: How should I approach programming questions during interviews?

Always remember: Think out loud whatever you are thinking.

You may be wrong but consider it to be a healthy discussion, the interviewer will help you along the way. Its almost never necessary to get the correct answer, most interviewers care about your basics and how you think.


3. Gayle Laakmann McDowell, founder/CEO of CareerCup, author of Cracking the PM Interview, Cracking the Coding Interview, and Cracking the Tech Career. 41189 views

Selected answer to: What are the top 10 pieces of career advice Gayle Laakmann McDowell would give to future software engineers?

#7: If you don't want to be a developer forever, then move on quickly. There is a lot of value in getting really deep technical expertise. But it doesn't matter that much whether you spent two years as a developer or seven years. Within a few years of college graduation, make a choice. Do you want to be an engineer for the next 10, 20, 30 years or not? If you don't, start trying to move on now. More time as an engineer won't help you that much.


4. Ricardo Vladimiro, Game Analytics and Data Science Lead @ Miniclip. 37838 views

Selected answer to: I'm not very good with maths and statistics. I'm a decent programmer. I want to become very good with machine learning / deep learning. Where should I begin and how can I continue?

Not being good at math and statistics is a fallacy. Try following: if you are interested in machine learning, start studying it and more important, practicing it. As you do it, you'll have to go back to math and statistics and review some things "you are not very good at".


5. Jeff Hammerbacher, Curious. 35599 views

Selected answer to: Why is Python a language of choice for data scientists?

Python is an interpreted, dynamically-typed language with a precise and efficient syntax. Python has a good REPL and new modules can be explored from the REPL with dir() and docstrings. That's one reason to prefer Python over C, C++, or Java.


6. Boxun Zhang, Data Scientist at Spotify; PhD in Computer Science. 29443 views

Selected answer to: What programming language is best for machine learning & statistical analysis? Is it R or Python?

Use R for statistical analysis and prototyping machine learning models; Use Python for implementing machine learning pipelines in production environment.

R is simply better than Python for data analysis, because R is designed at the beginning for statistical computing and has vast amount of 3rd-party packages for all kinds of statistical analysis.


7. Yisong Yue, Machine Learning Researcher. 25574 views

Selected answer to: Machine Learning: Is machine learning a field best suited for geniuses? Should I bother trying to pursue it?

Yes, you can absolutely make it! Work ethic and passion matter much more than raw mathematical & coding ability (whatever that is).

When I first started studying machine learning, I was overwhelmed by all the mathematical notation and abstract concepts. But over time, I gained a comfort level that allowed me to use math to reason very efficiently about different aspects of machine learning. After all, math is just a language, albeit one that is impeccably precise and rigorous.


8. Yilun (Tom) Zhang, Learning data science (Actively using R and Python), Statistics and Computational Math at University of Waterloo. 26901 views

Selected answer to: How long would it take to become a data scientist if I have a CS degree and entry level experience (1 year) working with datasets (though I haven't done much ML or modelling on them)?

With a degree in CS, you should be able to handle most of the programming part, it is just the matter of using a different set of libraries and packages. The more important part is to learn the concept and algorithm behind machine learning, and data processing.

I don't see it taking too long if you are passionate about it.


9. Alex Kamil. 24911 views

Selected answer to: How can I become a data scientist? (edited)

1) Learn about matrix factorizations
2) Learn about distributed computing
3) Learn about statistical analysis
4) Learn about optimization
5) Learn about machine learning
6) Learn about information retrieval
7) Learn about signal detection and estimation
8) Master algorithms and data structures
9) Practice
10) Study Engineering


10. Sean Owen, Director, Data Science @ Cloudera. 24564 views

Selected answer to: Can I get a job in data science without a master's and with a non-programming science degree?

It's hard to recommend a masters in stats or engineering as those may be too much of a jump. Consider "bootcamp"-like programs as a relatively cheaper, structured way to jump-start your background and try to get a job where you can invest time into learning through the rest of the background.

Fortunately data science is so in demand that it's a fungible quantity; people really want to hire smart, eager people who can work on the many problems surrounding data, and most roles don't require 'full stack data science'.


This list is based on Quora ranking of Data Science writers - thanks to Xamat Amatriain (Leading Engineering at Quora) for the tip.

Bio: Matthew Mayo is a computer science graduate student currently working on his thesis parallelizing machine learning algorithms. He is also a student of data mining, a data enthusiast, and an aspiring machine learning scientist.

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