I want to be a data scientist, but I have no programming or maths background. I have more than the basic understanding of python which I taught myself over a couple of months. Right now I am stranded, because I do not know how to frame a study plan?

I am working in a marketing firm with lots of data, so I have an income for the time that I study and I have data to apply my knew knowledge on. I am not trying to rush up and want to build up a strong base and grow upon it over a longer period of time. I know it is not going to be easy, but I feel strongly motivated.


Could you point out comprehensive learning plans with references to books, courses, and learning materials for self-teaching beginners? Could you also point out any articles or presentations in which former beginners and transitioners share their experiences about successfully becoming data scientists?

  • 1
    $\begingroup$ Take MOOCs in data science, algorithms, and data structures so you can learn things in a sensible order. Then get some books, solve some Kaggle challenges, and contribute to open source data science libraries on github. Because practice makes perfect. You are not going to be a data scientist in six months. You will barely have scratched the surface, so have realistic expectations. $\endgroup$
    – Emre
    Commented Jun 26, 2017 at 19:41
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    $\begingroup$ What makes you think you want to be a data scientist? In other words, what is it specifically that attracts you to the field? Think about what you'd like to be able to do and then research the necessary skills. Data science is a vast field with lots of different sub-disciplines and the path you follow really depends on which one you're in. Focusing on your specific goals will also help keep you motivated through the hundreds of hours of research/studying you will need to get through. $\endgroup$ Commented Jun 26, 2017 at 19:55
  • $\begingroup$ Data scientists are (should) be proficient mathematicians, which means that you should be prepared to get the equivalent of at least a bachelors in a math field. To be competitive, you will need at least an MS in stats/CS/hard science. $\endgroup$
    – Hobbes
    Commented Jun 26, 2017 at 20:31

1 Answer 1


Short Answer:

Practice. You already see it's both math and programming, so start practicing your math and your programming and eventually you'll be there.

Long Answer:

Data science happens at the intersection of math and programming.

A lot of people start with some intense math (biotech, physics, engineering, or if you're lucky statistics) and thus have to learn the programming from scratch.

A lot of others start with being some professional developer, and then have to learn the math.

But as you said, you know neither. So check your motivations. Do you really want to be a data scientist, or are you just caught up in the buzz that still surrounds it? You really have to love it to endure the massive learning curve. And if you love it, figure out why you're only now getting started.

Alright. Still on board? If so, you really have three paths as I see it.

  1. you can follow along with some mooc or series of tutorials to spoon feed you some basic project. It is a start.

  2. You can first try to break into being an analyst first. Could be a much shorter path. Best case scenario, analysts can turn directly into data scientist. Worst case scenario, you get stuck not quite living your dream. If you take this route with the intention of eventually becoming a data scientist, be verrry wary of the tools you use. Highly automated analyst tools keep you locked into what the tools can do. And if the tools don't have any statistics or machine learning baked in, then you'll get stuck.

  3. The hard way. If you're serious about it, then know you need to learn both overlapping languages - math and programming. Ask around and do your research. Find what programming languages will work best in the niche you expect you'll most like. A part of you "do I really want to be a data scientist?" should includes the types of things you want to do as a data scientist. Out of your answer, pull a few examples of actual projects. Of those projects, pick the easiest and learn the math behind it. Like financial stuff? Perhaps some simple time series prediction on some open financial data. Like marketing? Perhaps start with looking up A/B tests. Yes, this one may include also doing #1, but with the very important extra layer of sincerity around why you're doing it. And a LOT of the job of a data scientist is being able to take a question nobody's asked yet, and figure out how to apply your tools to solve it. Many moocs rob you of that most-necessary layer of first framing the problem before trying to solve it.

  • $\begingroup$ Emotional note: That feeling of being stranded always (for me) means I've bitten off too much and need to drop scope. Perhaps this would make more sense if you first become a dev or analyst? $\endgroup$ Commented Jun 26, 2017 at 20:39

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