Learning Data Science
My personal collection of resources and information to learn the ways of the Data Scientist.
Machine and Deep Learning
- Machine Learning
Instructor: David Rosenberg (NYU & Bloomberg)
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build.
- Machine Learning Crash Course
- Intro to Data Science
Intro to Data Science (DS-GA-1001) for the NYU Center for Data Science
- Data Science
Instructor: Allen Downey and Manish Datt (Olin College)
Statistics and Probability
- Statistical and Mathematical Methods
Instructor: Carlos Fernandez-Granda (NYU)
This course introduces statistical and mathematical methods needed in the practice of data science. It covers basic principles in probability, statistics, linear algebra, and optimization.
- Data Science from Scratch
- Data Science for Business
- Introduction to Machine Learning with Python
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Neural Networks and Deep Learning by Michael Nielsen
- Filippo Broggini (ETH Zurich)