Coursework

Stanford University
CS 106A. Programming Methodology.
CS 106B. Programming Abstractions.
CS 109. Introduction to Probability for Computer Scientists.
CS 110. Principles of Computer Systems.
CS 221. Artificial Intelligence: Principles and Techniques.
CS 229. Machine Learning.
CS 224N. Natural Language Processing with Deep Learning.
CS 224S. Spoken Language Processing.
CS 224W. Analysis of Networks.
CS 246. Mining Massive Data Sets.
CS 329X. Machine Learning Systems Design.
EE 263. Introduction to Linear Dynamical Systems.
DATASCI 112. Principles of Data Science.

Harvard University
CS 50. Introduction to Computer Science.
CS 50 for JDs. Computer Science for Lawyers.
CS 50 for MBAs. Computer Science for Business Professionals.
CS 100. Introduction to Computer Science (Advanced).
CS 164. Software Engineering.
CS E-23a. Web Programming with Python and JavaScript.
CS E-33a. Systems Programming and Machine Organization.
CS E-39b. Mobile Application Development.
CS E-80. Unix/Linux Systems Programming.
CS E-75. Building Dynamic Websites.
CS E-76. Web Application Development with JavaScript.
CS E-295. Master’s Thesis in Digital Media Arts and Sciences.

Harvard T.H. Chan School of Public Health
DS 101. Introduction to Data Science (R Basics, Visualization, Probability, Inference, Modeling, Wrangling, Regression).

Gies College of Business, University of Illinois Urbana-Champaign
ACCT 100. Accounting Fundamentals.
ACCT 200. Accounting Principles I.
ACCT 300. Intermediate Accounting.
FIN 200. Fundamentals of Finance.
ECON 101. Introduction to Microeconomics.
ECON 102. Introduction to Macroeconomics.
FIN 101. Finance for Non-Business Majors.
FIN 200. Corporate Finance.

MIT OpenCourseWare
6.0001. Introduction to Computer Science and Programming in Python.
6.0002. Introduction to Computational Thinking and Data Science.
6.005. Software Construction.
6.006. Introduction to Algorithms.
6.009. Fundamentals of Programming.
6.031. Software Engineering.
6.033. Computer Systems Engineering.
6.034. Artificial Intelligence.
6.036. Introduction to Machine Learning.
6.041A. Introduction to Probability.
6.042J. Mathematics for Computer Science.
6.004. Computation Structures.
6.028. Operating System Engineering.
6.046J. Design and Analysis of Algorithms.
6.828. Operating Systems.
6.829. Computer Networks.
6.841. Advanced Complexity Theory.
6.852. Distributed Algorithms.
6.853. Advanced Data Structures.
6.864. Advanced Natural Language Processing.
6.868. The Society of Mind.
6.8000–6.803. Advanced Topics in Computer Science.
6.820. Foundations of Program Analysis.
6.825. Techniques in Artificial Intelligence.
6.832. Underactuated Robotics.
6.231. Dynamic Programming and Stochastic Control.
6.437. Inference and Information.
6.438. Algorithms for Inference.
6.867. Machine Learning.
6.869. Advances in Computer Vision.
15.071. The Analytics Edge.
15.075J. Statistical Thinking and Data Analysis.
IDS.012. Computational Thinking for the 21st Century.
IDS.131. Statistics, Computation, and Applications.
14.310x. Data Analysis for Social Scientists.
Mega-R1–R7. Foundations and Advanced Modules in Machine Learning.