resources.md

layout	page
title	Data Science Resources
permalink	/resources/

A collection of useful resources for learning about data science - contains only classes and books I have completed:

• Coursera
  • Data Science Specialization, by Johns Hopkins University
    A 10-course sequence building data science skill with R programming.
  • Machine Learning, by Andrew Ng and Stanford University
    An 11-week class covering machine learning algorithms using GNU Octave, an open-source equivalent to MATLAB. You'll get a good grounding in how these algorithms work.
  • Deep Learning, by Andrew Ng and deeplearning.ai
    A five-course sequence in deep neural networks, including introduction to TensorFlow and Keras.
• Udemy
  • Docker Mastery: The Complete Toolset from a Docker Captain
    What is Docker? You can use Docker to test out a new technology (e.g., Mongo, PostgreSQL, Elasticsearch, etc.) without needing to install or configure it. You can configure a deep neural network model in Docker, and easily deploy to AWS.
• Books
  • Ry's Git Tutorial
    This is an excellent project-based introduction to using git. It's concise, and will get you going with the essential tools in git.
  • The Linux Command Line: A Complete Introduction
    If you're serious about data science, sooner or later you're going to need to muck around in Linux. This book is an excellent introduction to the Linux command line, its essential tools, and bash scripting.
  • OpenIntro Statistics, 3rd Ed
    An excellent introduction or review of basic statistics.
  • Coding the Matrix: Linear Algebra through Applications to Computer Science
    Linear algebra is the foundation of nearly all machine learning. This book is an excellent resource.
  • R for Everyone: Advanced Analytics and Graphics
    Good overall introduction to the R programming language.
  • Python Crash Course: A Hands-On, Project-Based Introduction to Programming
    If you've never used Python before, this is an excellent book that covers the basics in an engaging, project-based manner.
  • The Python 3 Standard Library by Example
    Also available freely as PyMOTW-3, this is a great place to spend a few minutes a day until you've covered the whole thing.
  • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Ed
    Exactly What It Says on the Tin, this is a concise and well-written guide to core Python packages commonly used in data analysis.