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
- Curated Lists of Data Science, Machine Learning, Deep Learning and ...
- Data Science Cheat Sheet
- Cheat Sheet: Data Visualization with R
- R, Python, Machine Learning, Dataviz: Most Popular Resources
- Great Github list of public data sets
- The Guide to Learning Python for Data Science
- Learning R in Seven Simple Steps
- 9 Python Analytics Libraries
- A Tour of Machine Learning Algorithms
- 4 easy steps to becoming a data scientist
- Turning Unstructured into Structured Data
2. General Content
- Deep Learning: Definition, Resources, Comparison with Machine Learning
- 24 Uses of Statistical Modeling
- Lifecycle of Data Science Projects
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 11 Important Model Evaluation Techniques Everyone Should Know
- 50 Questions to Test True Data Science Knowledge
- 21 data science systems used by Amazon to operate its business
- 22 tips for better data science
- 10 Great Data Science Articles by Bernard Marr
3. Additional Reading
- State-of-the-Art Machine Learning Automation with HDT
- Fast Combinatorial Feature Selection with New Definition of Predict...
- Implementation of 17 classification algorithms in R
- Tests of Hypotheses Revisited
- 10 types of regressions. Which one to use?
- 17 short tutorials all data scientists should read (and practice)
- Tutorial: How to detect spurious correlations, and how to find the ...
- 15 Most Controversial Data Science Articles
- Biased vs Unbiased: Debunking Statistical Myths
- 16 analytic disciplines compared to data science
Enjoy the reading!
Comment
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Comment by Randall Harvey yesterday -
Great work Vincent, thank you for helping us learn and advance.
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Comment by Howard Fulks on April 12, 2017 at 12:34pm -
Good stuff, Vincent. Also, 42.
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Comment by Sameera Koluguri on February 5, 2017 at 10:09pm
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Thank you.
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Comment by Dongsun Lim on December 17, 2016 at 11:15pm -
Thank you so much for the great article!
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Comment by Edward Schwab on November 14, 2016 at 12:30pm
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Thank you for this information! I'm commenting here in order to read later when I'm not at work.
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Comment by Savita Kirpalani on June 16, 2016 at 10:14pm -
Vincent,
Thanks for consolidating the pieces together.
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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
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Comment by Rachael on May 26, 2016 at 7:27pm
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Thank you very much for the information!
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