6.419x Data Analysis: Statistical Modeling and Computation in Applications

General:

• Navigating Matplotlib by Brandon Rohrer
• 6.419x report template generator by M Powers

Module 1. Review: Statistics, Correlation, Regression, Gradient Descent

Observational Studies and Experiments

• Understanding Statistical Power and Significance Testing: an interactive visualization by Kristoffer Magnusson
• Power of a hypothesis test by melbapplets
• Visualizing Type I vs Type II tradeoffs

Hypothesis Testing

• Interpreting Confidence Intervals: an interactive visualization by Kristoffer Magnusson

Likelihood Ratio Test and Multiple Hypothesis Testing

• False Discovery Rates, FDR, clearly explained by StatQuest with Josh Starmer (Benjamini-Hochberg method)
• The False Discovery Rate: An Overview by Phil Anderson

Correlation and Least Squares Regression

Gradient Descent

• An Interactive Tutorial on Numerical Optimization by Ben Frederickson (scroll down to Gradient Descent section)

Recitation 1: Average Treatment Effect versus Average Treatment Effect for the Treated

Module 2: Genomics and High-Dimensional Data

Visualization of High-Dimensional Data

• Random Vectors
• Everything you did and didn't know about PCA by Alex Williams
• MDS and PCoA by StatQuest with Josh Starmer
• Machine Learning: Multidimensional Scaling by GeostatsGuy Lectures
• How to Use t-SNE Effectively by Martin Wattenberg, Fernanda Viegas, Ian Johnson

Methods of Classification on High-Dimensional Data

Clustering with High-Dimensional Data

• SciPy Hierarchical Clustering and Dendrogram Tutorial by Jörn Hees

Recitation: Demonstration of Data Visualization, Clustering, and Classification

• How to tune hyperparameters of tSNE by Nikolay Oskolkov
• t-SNE: The effect of various perplexity values on the shape

Module 3: Network Analysis

Graph Basics

• Graph Theory: 01. Seven Bridges of Konigsberg by Sarada Herke
• NetworkX Tutorial by Jacob Bank, Evan Rosen

Graph Centrality Measures

• Network Centrality by Systems Innovation
• Degree Centrality by John McCulloch
• Using Centrality Analysis to Fight Crime by Tom Sawyer Software

Spectral Clustering

• The Modern Algorithmic Toolbox Lectures #11: Spectral Graph Theory, Tim Roughgarden & Gregory Valiant

Graphical models

Jupyter Notebook: From Data to Networks, Using Python Networkx

• Graphs and Graph Algorithms (networkx tutorial)
• Stylish adjacency matrix graphs by Jonathan Chang

Module 4: Time Series

Introduction to Time Series: Trend, Seasonality, Stationarity, Autocovariance

Time Series: Statistical Models

• Time Series: Autoregressive models AR, MA, ARMA, ARIMA by Mingda Zhang
• Let’s Forecast Your Time Series using Classical Approaches by Ajay Tiwari

Introduction to Time Series Analysis 3

• Forecasting: Principles and Practice, Rob J Hyndman and George Athanasopoulos

Module 5: Environmental Data and Gaussian Processes

Environmental Data and Gaussian Processes

• A Visual Exploration of Gaussian Processes

Spatial Prediction

• The Kernel Cookbook: Advice on Covariance functions by David Duvenaud

Sensing and Analyzing global patterns of dependence

Recitation: Working with Gaussians, and introduction to Gaussian Process