# Becoming a data scientist using only StackExchange sites - what questions should I look at? [closed]

If I had a maths degree with a little foundation in statistics, what would be the top $100$ questions/posts on CrossValidated or MathStackExchange or MathsOverflow or Stack Overflow that I would have to study in order to become a data scientist?

And if not possible, could you give me an idea of the steps (skills, knowledge, courses) that I would need for different career paths? For example research data scientist, data scientist in a start up, machine learning scientist, etc?

## closed as primarily opinion-based by Aksakal, Xi'an, Tim♦, Sycorax, Haitao DuApr 19 '17 at 14:44

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.

• I don't think this is the right approach to 'become' a data scientist. The way I am doing it is that I had started with a few (but good quality) online courses on data science/machine learning/statistics to understand the basics and then find a couple of problems to work my skills on. Only then you should start consulting stackexchange, when you have a specific question arising from your problem. – DimP Apr 19 '17 at 14:05
• @Aksakal And what about the statistics tool set, is it secondary? – Euler_Salter Apr 19 '17 at 14:11
• You won't learn it by reading any top 100 Q&A. – Tim Apr 19 '17 at 14:13
• Statistics is important BUT programming is a disqualifying skill for a data scientist. If you can't program you're useless, while without strong statistics you're still useful as a data scientist – Aksakal Apr 19 '17 at 14:14
• @Euler_Salter from the top 100 questions you will learn how to solve 100 specific problems, or answer 100 questions. It is as you learned 100 most common words in Japanese and pretended that you knew the language! – Tim Apr 19 '17 at 14:17

First, it is hard to define what is data scientist. Different people have different definitions. See this question: What is a data scientist?

Then, you may want to think what kind of job you want to do: more on building statistical model? Or more on making software (engineering work) tuning on clusters to processing huge amount of data. In addition, what field are you interested in? e.g., Health care, education? An interesting diagram can be found here. Where "hacking skills" and "subject expertise" are also extremely important.

I personally do not think Kaggle Competition can be counted for "science". And blindly using pre-developed packages to make "accurate predictions" and climb on ladder board is meaningful work.

Steps to follow is really depending on your objective (dream job). Working in a big company as a research (data) scientist vs. working in a start-up that taking care of everything from server/cloud maintenance to talking to customer would be totally different.

If you want to become a research scientist in AI / machine learning, read top conference papers in the filed (Such as AAAI, NIPS, ICML, ICDM) get try to get a Ph.D. in machine learning / statistics / operations research. (We have an interesting question here Why do Statistics, Machine learning and Operations research stand out as separate entities)

If you want to become a data scientist in a start up company, staring with MOOC courses may be better. In most of those courses, they emphasize how to use it in real world instead of the theoretical background behind statistical methods. In addition, trying to learning how to use could, e.g., AWS and learn how to programming (e.g., in python / java). Finally, the communication skill is also very important.

To conclude: think more on the career path you want to have in the future. How much time you want to spend on reading paper and deriving math. How much time you want to spend to debug the code and configure the server. Which field / subject expertise are you thinking you want to get into. Then search related questions and answers to read.

• stake in the ground? :) – Aksakal Apr 19 '17 at 14:10
• @hxd1011 nice link, I never saw that venn diagram before. I would like to work on Machine Learning/ AI. I often find it confusing indeed to see the difference between someone working in ML or a data scientist. I'll have a read of your website and the question you linked, thanks! – Euler_Salter Apr 19 '17 at 14:14
• Yes the link probably gives you as much information as you will find on StackExchnage. – Michael Chernick Apr 19 '17 at 14:15
• @hxd1011 so what steps would you suggest me to follow? What online courses, what skills would I need to learn/enrol ? – Euler_Salter Apr 19 '17 at 14:18
• @hxd1011 I know it is a big effort, but if you could select the steps for some of these different careers (for example research data scientist, working in start-up, machine learning scientist, AI scientist) it would be great! – Euler_Salter Apr 19 '17 at 14:25