Python & Data Science
Notes along the Data Science, Python, R journey
Books
Collateral for Books
- An Introduction to Statistics with Python (Thomas Haslwanter)
- Numerical Python (J Robert Johansson)
- Python for Data Analysis, 2nd Edition (Wes McKinney)
- Introduction to Python for Econometrics, Statistics and Numerical Analysis: Third Edition (Kevin Sheppard)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (Aurélien Géron)
MOOCs
- Intro to Python for Data Science (Datacamp - Filip Schouwenaars)
- Foundations of Data Analysis - Part 1 (edX - UTAustinX - Dr. Michael Mahometa)
- Statistical Learning (Stanford - Trevor Hastie, Robert Tibshirani)
- Statistics and Probability (Khan Academy)
- Statistics and Probability Courses (cK12)
- Machine Learning (Coursera - Stanford - Andrew Ng)
- Single Variable Calculus (MIT Open CourseWare)
- Multi Variable Calculus (MIT Open CourseWare)
- Introduction to Probability and Statistics (MIT Open CourseWare)
- Linear Algebra (MIT Open CourseWare)
Competitions
Portals/Blogs
- Analytics Vidhya
- Data School
- KDNuggets
- Machine Learning Mastery
- NumFOCUS (Sponsors of several Python related projects)
- PyData
Software
- Python
- R (The Comprehensive R Archive Network)
- R Studio and Shiny
- Python and R based Data Science toolkit (Anaconda)
- Tableau
- SAS
- MATLAB (MathWorks)
- Mathematica (Wolfram)
- Octave (GNU)
- Gephi
Important Python Libraries
Core Scientific Computing
Web Frameworks
Datasets
- Stanford Large Network Dataset Collection
- UCI Network Data Repository
- UCI Machine Learning Repository
- Matrix Market
- enigma
Documentation & Tutorials
Python
Libraries
R
Important Resources
Python Standards
Learning Resources
- Scipy Lecture Notes
- scikit-learn Algorithm Cheatsheet
- 28 Jupyter Notebook tips, tricks, and shortcuts (Dataquest)
- pythontutor.com
Videos
- Python Related Videos (pyvideo.org)
- Machine Learning Lectures by Andrew Ng at Stanford
- Transforming Code into Beautiful, Idiomatic Python (Raymond Hettinger)
- Inside NumPy (Nathaniel Smith)