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Machine Learning(Stanford University)

Statistical Learning(Stanford University)

Scalable Machine Learning(UC Berkeley)

Introduction to Computer Science and Programming Using Python(MIT)

Introduction to Computational Thinking and Data Science(MIT)

Data Analysis(Johns Hopkins University)

Data Science Specialization(Johns Hopkins University)

The Data Scientist’s Toolbox

R Programming

Getting and Cleaning Data

Exploratory Data Analysis

Reproducible Research

Statistical Inference

Regression Models

Practical Machine Learning

Developing Data Products

Data Science Capstone

Data Analysis for Genomics(Harvard University)

Data Analysis for Life Sciences(Harvard University)

Statistics and R for the Life Sciences

Introduction to Linear Models and Matrix Algebra

Advanced Statistics for the Life Sciences

Introduction to Bioconductor

Case study: RNA-seq data analysis

Epidemiology (University of North Carolina at Chapel Hill & Boston University)

Computational Photography(Georgia Institute of Technology)

Computing for Data Analysis(Johns Hopkins University)

Case-Based Introduction to Biostatistics(Johns Hopkins University)

Mathematical Biostatistics Boot Camp I(Johns Hopkins University)

Mathematical Biostatistics Boot Camp II(Johns Hopkins University)

Bioinformatics: Life Sciences on Your Computer(Johns Hopkins University)

Chemicals and Health(Johns Hopkins University)

Living with Dementia: Impact on Individuals, Caregivers, Communities and Societies(Johns Hopkins University)

Major Depression in the Population: A Public Health Approach(Johns Hopkins University)

Introduction to Psychology(Yale University)

Introduction to Linux(The Linux Foundation)

The Art of Photography(RMIT University)

Writing in the Sciences(Stanford University)

Model Thinking(University of Michigan)

Welcome to Game Theory(The University of Tokyo)

Ancient History of China(Peking University)

Traditional Chinese Medicine and Chinese Culture(Shanghai Jiao Tong University)

Basic Life Support

Introduction to Project Management(University of Adelaide)

Systems Thinking In Public Health (Johns Hopkins University)

Introduction to Complexity(SFI)

Game Theroy I (Stanford University, The University of British Columbia)

Game Theory II: Advanced Applications (Stanford University, The University of British Columbia)

PH559x Causal Diagrams: Draw Your Assumptions Before Your Conclusions (Harvard University)

Financial Markets(Yale University)

Network Science (Albert-László Barabási)

Interfacing with the Raspberry Pi (University of California, Irvine)

Crash Course: Economics

Crash Course: Ecology


My MOOC notes






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