This is material for scientists, analysts, and developers who have some experience with Go and statistics and want to learn how to do data science with Go. We believe these classes are perfect for data scientist interested in working in Go or Go programmers interested in doing data science.
Note: This material has been designed to be taught in a classroom environment. The code is well commented but missing some of the contextual concepts and ideas that will be covered in class.
This material introduces the basic principles and procedures of data science and motivates the use of Go in the context of data science. Once you are done with this material you will understand what data science is and how Go can help data scientists maintain integrity in the applications they develop.
This material covers the gathering, organization, and parsing of data to/from a local and remote sources. Once you are done with this material you will understand how to interact with data stored in various places and in various formats, how to parse and clean that data, and how to output that cleaned and parsed data.
Data Gathering, Organization, and Parsing
This material covers the organization of data into matrices and matrix operations. Once you are done with this material you will understand how to form matrices within Go programs and how to utilize those matrices to perform various types of matrix operations.
This material covers statistical measures and operations key to day-to-day data science work. Once you are done with this material you will understand how to perform solid summary data analysis, describe and visualize distributions, quantify hypotheses, and transform data sets with, e.g., dimensionality reductions.
This material introduces various types of machine learning along with evaluation and validation techniques. Once you are done with this material you will understand how to train, evaluate, validate, and utilize various models (e.g., for regression, clustering, and classification).
All material is licensed under the Apache License Version 2.0, January 2004.