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.
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!