What should I do next?

Hi Guys!

So I am done with the Data Science path on Codeacademy, and would want to continue with the learning process. I am more interested in the application of data science in businesses, digital marketing, and machine learning. I have majored in Economics & Mathematics, and hence carry a mathematical background.

Please suggest me the way forward to continue my learning of Python, SQL, and R in the relevant fields. Any sort of help would be appreciated. Thank you!

Hello @msohaibs96, welcome to the forums! There are a few things you could do. If you wanted to increase your knowledge of the languages, you could take the individual course on Codecademy (R, SQL and Python). There are also quite a lot of courses relating to SQL and Python (just search for Python or SQL in the catalogue to find the full range of courses/paths available). If I am not mistaken, there is a Machine Learning course. There are also some projects on Codecademy that you could do to challenge yourself, which leads to another thing you could do; projects. Either doing the CC projects, a project you found on the internet, or your own project, projects are always a good way to build on and cement the knowledge you have gained. If you wanted to do some sort of certification, you could take the courses offered on places like Coursera.

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Hey there @msohaibs96, my two cents on a machine learning track:

In tandem with the paths and courses here, I think tuning in to OCDevel’s podcast on high level fundamentals of Machine Learning is worth considering. His episode on certificates and degrees is a great rundown on what’s next for students like us.

I also recommend Andrew Ng’s Coursera course, which seems to be an ML requisite, following much of what I’ve read (also referenced by @kingdarboja in this Machine Learning post). Heavy on the maths, and he uses Octave/Matlab, but you get the gist; R, Matlab, Python or otherwise.


Build something that interests you, from scratch.

Codecademy provides a great foundation for entering the field of data science, but there is only so much they (or any online resource, for that matter) can cover.

If you are interested in applying data science to digital marketing, then go out and build something that demonstrates how to do that, or that attempts to improve a process by using machine learning. Browse the web for datasets that might work for your project, and if you can’t find any, consider making your own by scraping a website or two.

The key is really to go start building on your own — that is the best way to continue with the learning process. Once you start, you will quickly find out exactly what your strengths and weaknesses are, and you can tailor any further learning from there.