AI News, Data science intro for math/phys background

Data science intro for math/phys background

After posting What I do or: science to data science I got a lot of emails on how to make this transition.

In short: All projects required me to learn something new - be it a library, a machine learning model or a software tool.

From my perspective the whole process looks that way: And everything needs to be done in a reproducible way - so others can interact with your code, or even run it on a server.

even look at this tweet - while humorous2, it shows a balanced list of typical skills and activities of a data scientist:

If you want to learn more about what is data science, look at the following links: When you have some academic title, no-one will question your intelligence.

From my experience, you need to fulfill two requirements: Most data science things are simple and at the point that you are able to use R or Python you can start working, gradually increasing your knowledge and experience.

So (from Academia to Industry linked below): While having a strong coding ability is important, data science isn’t all about software engineering (in fact, have a good familiarity with Python and you’re good to go).

Things need to work, and there is little difference if it is based on an academic paper, usage of an existing library, your own code or an impromptu hack.

That is, in data science the emphasis is on practical results (like in engineering) - not proofs, mathematical purity or rigor characteristic to academic science.

Rather than being a complete record or all positions, awards and publication, it is a short (typically 1 page) summary of the main skills and the most important positions/accomplishments.

In any case, take a look at: If you need learn basic algorithms and data structures, I recommend: If you get no technical questions, it may be a red flag. If

you get only software engineering questions, it may be a sign that they want to hire a programmer, not - a data scientist (no matter what their job calling says); and

won’t point to a general tutorials - there are tons of it and personal preferences vary (MOOCs, interactive courses, websites, textbooks, …) and I tried to link only to things I recommend myself. When

Learn to be comfortable with Python (installing packages, loading, saving and transforming data, etc) - links below may help: You need some basic linear algebra (vectors, matrices, SVD, …), calculus (exp, log, differentiation, integration, …) probability (independence, conditional probability, …), but if you are from natural science background, you already know that. It

does not mean that you know all - it just means that right now you have mathematical skills sufficient to be an employable data scientists and you are able to read about other methods, algorithms, etc.

If you need to get a real dataset suitable for working with a given machine learning algorithm, there is a wonderful collection: For statistics, screw learning by heart various statistical distributions and tests - you can easily look them up later.

For the latter I recommend: It’s a fast changing field - I am constantly tracking new libraries and updates to ones I am using.

get me wrong - there are great resources, it provides feedback (otherwise it is hard to tell if your solution is good) and some people find it really engaging. But

Also, this way it is a complete data science - from asking questions and getting data to presenting the results in a meaningful form.

So don’t be afraid to asking about or for anything, starting talking to people etc - on the average it will be much better than taking a passive posture.

Here is a random list of starting points I consider interesting: EDIT (Feb 2018) - some of my new introductions: This blog post started as emails, and went through a stage of an extract of emails (shared on Google Docs).

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