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!

63 members like this

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

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: Vincent,
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.

Harshendu