🌟 Data Science Roadmap
  August 19, 2017  2 minutes read    Data Science   Github

I am one of those that enjoy searching the web for valuable resources in areas of my interest, in particular, Data Science. I think that getting an interesting link of a course cannot just stay forgotten in my bookmarks list. There has to be a better way to have those links near and make some use of this search process. That is why I have thought to create a repo in github to list courses, books, etc., related to Data Science and track my progress with them. Something like a list of completed courses, courses that I intend to dedicate time to in the following weeks, etc. But also, courses with a syllabus of the content of each of them. It happens to me that I bump into a concept, apparently a new one, but when I review it in depth I happen to discover that I had already studied it in the past, or maybe something quite related although I cannot remember when and where to review past material.

This is an idea inspired to similar ones that I have seen from others. For example, these two projects:

• Coding Interview University.
• Top-down learning path: Machine Learning for Software Engineers.

You can inspect these interesting repos and the corresponding reasons of the authors to create them. I think my goal may be somehow similar.

Another subject I did not find that easy at first was to order things in a proper way. I mean, there are many intermixed disciplines, courses that might correspond to more than one category, etc. I think I am happy with the current classification that I have established to order this information, as you can see in the TOC.

Too much information that I wanted to have more manageable and ordered to my taste. If this is also something inspiring for someone else, so much the better. The link to my personal Data Science roadmap is accessible through this link, but also via the Map option in the navigation menu above. The main categories in which I have structured the information are:

1. Introductory Courses.
2. General Courses.
3. Data Analysis.
4. Machine Learning.
5. Text Mining and NLP.
6. Data Visualization and Reporting.
7. Probability and Statistics.
8. Big Data.
9. Miscellaneous.
10. Books.

For each course entry, I have included the link to it, the syllabus as I have mentioned above, the author and institution, and sometimes an estimation of the shortest time required for it. This latest info is based on what appears for example in DataCamp, Coursera, edX, Udacity, or even Class Central. Some courses may not include this information and I have preferred not to give any wrong estimation.

Regards!