Daniel Compton

Heavily inspired by Scott Young’s MIT Challenge I have prepared a data science curriculum for study, nominally over the next twelve months but it may take longer. I used The Open Source Data Science Masters Curriculum as a base but modified it a little to lean towards my own interests and leave out things I already know. The course is made up of a mix of pure textbook study, MIT Open Courseware courses, Coursera courses and a few other learning resources. I’ll update it as I work with results and other changes.

• ☐ Math
  • ☐ Proofs
    • ☐ How To Prove It - Daniel J. Velleman, and solutions
  • ☐ Linear Algebra
    • ☐ Lay - Linear Algebra
    • ☐ 18.06 Linear Algebra
    • ☐ Burke Lecture Notes
    • ☐ Coding The Matrix
    • ☐ Quantifying Uncertainty
  • ☐ Statistics
    • ☐ Stats in a Nutshell
    • ☐ Think Stats
    • ☐ Think Bayes
    • ☐ Intro to Statistical Learning
    • ☐ Forecasting: Principles and Practice
  • ☐ Calculus refresher
  • ☐ Problem Solving
  • ☐ Concrete Mathematics
• ☐ Computing
  • ☐ Algorithms
    • ☐ Introduction To Algorithms MIT OCW
    • ☐ Algorithms - Coursera
    • ☐ Algorithms, Part II - Coursera
    • ☐ Algorithm Design - Jon Kleinberg
    • ☐ SICP book and lecture videos
  • ☐ Distributed Computing
    • ☐ Distributed Computer Systems Engineering OCW
    • ☐ Distributed Algorithms OCW
  • ☐ Databases
    • ☐ Cassandra
    • ☐ Neo4j
    • ☐ Riak
    • ☐ HBase
    • ☐ Database Management Systems - Raghu Ramakrishnan, Johannes Gehrke
  • ☐ Data Mining
    • ☐ 15.062 Data Mining OCW
    • ☐ Elements of Statistical Learning: Data Mining, Inference, and Prediction
    • ☐ Mining Massive Data Sets
    • ☐ Mining The Social Web
    • ☐ Introduction to Information Retrieval
  • ☐ Artificial Intelligence
    • ☐ AI Fall 2010 OCW
  • ☐ Machine Learning
    • ☐ Machine Learning - Coursera. Started 2014-03-03
    • ☐ Machine Learning MIT OCW
    • ☐ Machine Vision OCW
    • ☐ Prediction: Machine Learning and Statistics
    • ☐ Bishop’s Pattern Recognition and ML
    • ☐ Neural Networks
  • ☐ Probabilistic Graphical Models
    • ☐ Bayesian Methods for Hackers
    • ☐ Probabilistic Graphical Models
  • ☐ Natural Language Processing
    • ☐ Advanced NLP OCW
    • ☐ Natural Language Processing with Python
  • ☐ Analysis
    • ☐ Analyzing Big Data With Twitter
    • ☐ Social and Economic Networks
    • ☐ Python for Data Analysis
  • ☐ Geospatial Analysis
  • ☐ Visualization
    • ☐ Data Viz
    • ☐ Information Visualisation - Tufte
    • ☐ Data Visualization
    • ☐ D3 Tutorial
    • ☐ Berkeley Viz Class
  • ☐ Python
    • ☐ Learn Python the Hard Way
  • ☐ R
    • ☐ The Art of R Programming
    • ☐ R in a Nutshell
    • ☐ ggplot2
  • ☐ Clojure
    • ☐ Data Sorcery with Clojure
    • ☐ Clojure Data Analysis Cookbook
  • ☐ Final project