## Tutorials and References

Python Tutorial hosted by Tutorials Point. A step by step tutorial with a table of contents that serves as entries into well defined definitions and examples. Uses Python 2.4.3, with no GUI, otherwise excellent.

Kaggle — The Home of Data Science and Machine Learning.

- Competitions — many with real money prizes.
- Data sets — thousands of them.
- Education — High quality, hands-on, data science education.

Foundations of Data Science. UC Berkeley

From their website: The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

The material includes an excellent, and free, online book: Computational and Inferential Thinking.

Pandas — Greg Reda’s “Intro to Pandas Data Structures”

- Part 1: Intro to pandas data structures, covers the basics of the library’s two main data structures – Series and DataFrames.
- Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data.
- Part 3: Using pandas with the MovieLens dataset, applies the learnings of the first two parts in order to answer a few basic analysis questions about the MovieLens ratings data.

Mathematics for Machine Learning: Linear Algebra. Coursera. Uses Python.

Mathematics for Machine Learning. Coursera. Three course sequence (linear algebra, multivariate calculus, principal component analysis) from Imperial College of London. Uses Python.

Linear Algebra — MIT Open CourseWare. MIT Course 18.06, Spring 2010, Prof. Gilbert Strang. Uses Matlab.

MIT Course 18.06 SC. Fall 2011, Linear Algebra “Scholar Course” — designed for independent learners.

Probability and Statistics — MIT Open CourseWare. MIT Course 18.05, Spring 2014, Dr. Jeremy Orloff and Dr. Jonathan Bloom. Uses R.

Calculus — MIT Open CourseWare. MIT 18.01SC, Fall 2010, Prof. David Jerison.

Computer Science (Introduction) and Programming in Python — MIT Open CourseWare. MIT 6.0001, Fall 2016, Dr. Ana Bell.

Artificial Intelligence. MIT 6.034, Fall 2010, Dr. Patrick Winston.