#Beginner Material
(Back to Master Table of Contents)
Beginner Python
Beginner R
Beginner Statistics
Beginner Machine Learning
Beginner Visualization
Think Python 2e
by Allen Downey
"Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters." Uses Python 3.
Other tags: Python Textbooks
Think Python 1e
by Allen Downey
"Think Python is an introduction to Python programming for beginners. It starts with basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters." Uses Python 2.
Other tags: Python Textbooks
Introduction to Computer Science and Programming Using Python
by Eric Grimson, John Guttag, and Ana Bell (MIT)
"What you'll learn: A Notion of computation, The Python programming language, Some simple algorithms, Testing and debugging, An informal introduction to algorithmic complexity, Data structures"
Other tags: Python Courses
Introduction to Python for Data Science
by Filip Schouwenaars, Microsoft
"In this practical course, you will start from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames. Along the way, you'll learn about Python functions and control flow. Plus, you'll look at the world of data visualizations with Python and create your own stunning visualizations based on real data."
Other tags: Python Courses
Computational Statistics in Python
by Cliburn Chan (Duke University)
Thorough tutorial of Python from basics through scientific stack. Uses Python 3.
Other tags: Python Tutorials
Python For SAS Users
by Randy Betancourt
Presentation of Python data manipulations with pandas and their SAS counterparts
Other tags: Python Tutorials
Scipy lecture notes
by Chris Burns, Christophe Combelles, Emmanuelle Gouillart, and Gaël Varoquaux
"Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert."
Other tags: Python Tutorials
How to Think Like a Computer Scientist: Interactive Edition
by Brad Miller and David Ranum
Interactive version of the book How to Think Like a Computer Scientist by Jeffrey Elkner, Allen B. Downey, and Chris Meyers
Other tags: Python Tutorials
Official Python Documentation
by Python Software Foundation
Tutorials and Documentation for Beginner, Moderate, and Advanced Users. Available for Python 2 and 3.
Other tags: Python Tutorials
A Complete Tutorial to Learn Data Science with Python from Scratch
by Kunal Jain
Covers basics through predictive modeling in Python 2.
Other tags: Tutorials in Python
Introduction to R for Data Science
by Filip Schouwenaars, Microsoft
"This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required."
Other tags: R Courses
Data Analysis and Visualization Using R
by David Robinson and Neo Christopher Chung (Princeton University)
"This course combines video, HTML and interactive components to teach the statistical programming language R."
Other tags: R Courses
Beginner's guide to R: Introduction
by Sharon Machlis
"Our aim here isn't R mastery, but giving you a path to start using R for basic data work: Extracting key statistics out of a data set, exploring a data set with basic graphics and reshaping data to make it easier to analyze."
Other tags: R Tutorials
Advanced Beginner's Guide to R
by Sharon Machlis
"Learn how to wrangle data, including using R's transform, apply and mapply functions, along with sorting, grouping by date range and reshaping. We also take you through some dplyr basics."
Other tags: R Tutorials
Think Stats 2e
by Allen Downey
"Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding."
Other tags: Statistics Textbooks
Think Bayes
by Allen Downey
"Think Bayes is an introduction to Bayesian statistics using computational methods. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops."
Other tags: Statistics Textbooks
Computational and Inferential Thinking
by Ani Adhikari and John DeNero
"Data are descriptions of the world around us, collected through observation and stored on computers. Computers enable us to infer properties of the world from these descriptions. Data science is the discipline of drawing conclusions from data using computation. There are three core aspects of effective data analysis: exploration, prediction, and inference. This text develops a consistent approach to all three, introducing statistical ideas and fundamental ideas in computer science concurrently. We focus on a minimal set of core techniques that they apply to a vast range of real-world applications. A foundation in data science requires not only understanding statistical and computational techniques, but also recognizing how they apply to real scenarios." Uses Python 3.
Other tags: Python Textbooks, Statistics Textbooks
Data Skeptic
by Kyle Polich
"Data Skeptic is a podcast that alternates between short mini episodes and longer interviews. For the mini-episodes, Kyle and Linh Da explore basic data science concepts. Longer interviews feature practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism."
Other tags: Statistics Podcasts
A Table of Narratives and Generated Distributions
by Ben Klemens
"This project lists open-form narratives and the closed-form distributions that approximate them. Its intent is to help you build estimable statistical models on a sound micro-level foundation."
Other tags: Statistics Misc
Statistics for Hackers - PyCon 2016
by Jake Vanderplas
"Statistics has the reputation of being difficult to understand, but using some simple Python skills it can be made much more intuitive. This talk will cover several sampling-based approaches to solving statistical problems, and show you that if you can write a for-loop, you can do statistics."
Other tags: Statistics Lectures
A First Encounter with Machine Learning
by Max Welling
A nice introduction to various algorithms, with intuitive explanations of the formulas.
Other tags: Machine Learning Textbooks
An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
"This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist."
Other tags: Statistics Textbooks, Machine Learning Textbooks, R Textbooks
scikit-learn algorithm cheat sheet
Flow chart for Machine Learning Algorithms in scikit-learn.
Other tags: Machine Learning Misc
Machine Learning
by Andrew Ng, Stanford University
"This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas."
Other tags: Machine Learning Courses
Learning From Data (Introductory Machine Learning)
by Yaser S. Abu-Mostafa (Caltech)
"Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just 'knowing.'"
Other tags: Machine Learning Courses
Choosing a Data Chart
by Unknown
Flow chart for choosing a chart type.
Other tags: Visualization Misc
