Hitchhiker's Guide to Data Science, Machine Learning, R, Python

Thousands of articles and tutorials have been written about data science and machine learning. Hundreds of books, courses and conferences are available. You could spend months just figuring out what to do to get started, even to understand what data science is about.

In this short contribution, I share what I believe to be the most valuable resources - a small list of top resources and starting points. This will be most valuable to any data practitioner who has very little free time. 

Map-Reduce Explained

These resources cover data sets, algorithms, case studies, tutorials, cheat sheets, and material to learn the most popular data science languages: R and Python. Some non-standard techniques used in machine-to-machine communications and automated data science, even though technically simpler and more robust, are not included here as their use is not widespread, with one exception: turning unstructured into structured data. We will include them, as well as Hadoop-based techniques (distributed algorithms, or Map-Reduce) in a future article. 

1. Technical Material

2. General Content

3. Additional Reading

Enjoy the reading!

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Comment by Randall Harvey on October 16, 2018 at 4:14am

Great work Vincent, thank you for helping us learn and advance.

Comment by Howard Fulks on April 12, 2017 at 12:34pm

Good stuff, Vincent. Also, 42.

Comment by Sameera Koluguri on February 5, 2017 at 10:09pm

Thank you.

Comment by Dongsun Lim on December 17, 2016 at 11:15pm

Thank you so much for the great article! 

Comment by Edward Schwab on November 14, 2016 at 12:30pm

Thank you for this information! I'm commenting here in order to read later when I'm not at work.

Comment by Savita Kirpalani on June 16, 2016 at 10:14pm
Thanks for consolidating the pieces together.
Comment by Harshendu Desai on May 31, 2016 at 9:04am

Hello Vincent, 

I like your explanation about Map Reduce by picture. However ,  Is  it little confusing  applying  so many methodology  to data sets  using different , different tools and techniques ? Yes, we can do programming in R, SAS and SPSS for statistical analysis and somewhat with Python as well.  As a data science project for predictive analysis looks like leads to subjective analysis and confusing to end users with different, different result sets.


Comment by Rachael on May 26, 2016 at 7:27pm

Thank you very much for the information!

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