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!
Comment
Great work Vincent, thank you for helping us learn and advance.
Good stuff, Vincent. Also, 42.
Thank you.
Thank you so much for the great article!
Thank you for this information! I'm commenting here in order to read later when I'm not at work.
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
Thank you very much for the information!
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