UChicago Courses
UChicago being a quant powerhouse – yah, we really are! – is evident in the sheer volume of Analytics-related content available through the entire system. We've classified courses into the following categories, and decided to list them alphabetically in each category instead of being overly analytical about the ordering:
- Statistics
- Economics & Econometrics
- Marketing Analytics
- Finance & Stochastic Processes
- Data Mining & Predictive Analytics
- Machine Learning, Artificial Intelligence (A.I.) & Robotics
- Computer Science & Computer Programming
- Data & Computing Technologies
- Data Visualization
- Mathematical, Numerical & Optimization Methods
- Other Industry-Specific Analytics
The UChicago departments / institutes you'll want to befriend when looking for Anaytics-related UChi courses are:
- Economics department ("ECON")
- Becker Friedman Institute
- Graham School of Continuing Studies: MSc Analytics ("MSCA")
- Computer Science department ("CMSC" / "MPCS")
- Statistics department ("STAT")
- Toyota Technological Institute ("TTIC")
- Mathematics department ("MATH") / Financial Mathematics ("FINM")
- Computation Institute, a joint UChicago-Argonne National Lab research house
- Harris School of Public Policy Studies: Computational Analysis & Public Policy ("CAPP") & Data Science for Social Good
- Urban Center for Computation & Data
You can check all UChicago courses' schedules here.
- Foundations of Computational Data Analysis (MPCS 53110)
- Non-Parametric Inference (STAT 37400)
- Marketing Analytics (MSCA 32003)
- Applied Algorithmic Trading (FINM 35910)
- Brownian Motion & Stochastic Calculus (STAT 38510)
- Case Studies of Implementation in Computational Finance (FINM 37701)
- Computing for Finance (FINM 32300)
- Data Analysis for Finance & Statistics (FINM 33180)
- Financial Analytics (MSCA 32001)
- Fixed Income Derivatives (FINM 33601)
- Foreign Exchange & Fixed Income (FINM 37300)
- Gaussian Processes (STAT 34000)
- Mathematical Foundations of Option Pricing (FINM 33000)
- Mathematical Market Microstructure: An Optimization Approach (FINM 37601)
- Mathematical Market Microstructure without Rationality Assumptions (FINM 37602)
- Portfolio Theory & Risk Management I (FINM 36700)
- Portfolio Theory & Risk Management III (FINM 36702)
- Regression Analysis & Quantitative Trading Strategies (FINM 33150)
- Risk Analytics (MSCA 32004)
- Signals, Systems & Random Processes (TTIC 31090)
- Statistical Risk Management (FINM 33400)
- Statistics of High Frequency Financial Data (FINM 33170 / STAT 33970)
- Stochastic Calculus (FINM 34500)
- Stochastic Processes [, Introduction to] (STAT 31200)
- Time Series Analysis & Forecasting (MSCA 31006)
- Time Series Analysis & Stochastic Processes (MPCS 58020)
- Advanced Data Analytics (MPCS 53112)
- Advanced Data Mining & Predictive Analytics (MSCA 31009)
- Big Data (CSPP 53013)
- Big Data & Text Analytics (MSCA 32011)
- Data Mining (CMSC 33510)
- Data Mining Principles (MSCA 31008)
- Real-Time Analytics (MSCA 32005)
- Artificial Intelligence [, Introduction to] (CMSC 35000)
- Artificial Intelligence [, Topics in] (CMSC 35900)
- Computational Linguistics (CMSC 35050)
- Computer Vision [, Introduction to] (CMSC 25040)
- Computer Vision [, Introduction to] (CMSC 35040 / TTIC 31040)
- Statistical Machine Learning [, Introduction to] (TTIC 31020) by Greg Shakhnarovich
- Learning & Inference in Graphical Models (TTIC 31140)
- Machine Learning (CMSC 25400)
- Machine Learning (STAT 27725)
- Machine Learning (CMSC 35400)
- Machine Learning (STAT 37710)
- Machine Learning & Large-Scale Data Analysis (CMSC 25025 / STAT 37601) by John Lafferty
- Natural Language Processing (NLP) (TTIC 31190) by Kevin Gimpel
- Pattern Recognition (STAT 24610)
- Pattern Recognition (STAT 37500)
- Probabilistic Graphical Models (TTIC 31180) by Matt Walter
- Planning, Learning & Estimation for Robotics and Artificial Intelligence (TTIC 31170) by Matt Walter
- Speech Technologies (CMSC 35900 / TTIC 31110)
- Statistical & Computational Learning Theory (TTIC 31120)
- Statistical Machine Learning [Topics in ] (STAT 37790)
- Statistical Methods in Artificial Intelligence (CMSC 35420)
- Visual Recognition (TTIC 31130)
- Algorithms (CMSC 37000 / TTIC 31010)
- Algorithms (MPCS 55001)
- Applied Parallel Programming (CSPP 51085)
- Computability & Complexity Theory (CMSC 38500, MATH 30500, TTIC 31060)
- Information & Coding Theory (CMSC 37220 / TTIC 31200)
- Quantum Computing (CMSC 38410)
- Python Workshop (MSCA 37003)
- R Workshop (MSCA 31003)
- R Workshops (MSCA 37006)
- SAS Workshop (MSCA 37000)
- Short Python Course (MSCA 37009)
- Short R Workshop Course (MSCA 37007)
- Advanced Databases (MPCS 53003)_
- Advanced Distributed Systems (CMSC 23310)
- Advanced Distributed Systems (CMSC 33310)
- Big Data (MPCS 53013)
- Cloud Computing (CSPP 51083 / MPCS 51083)
- _Data Mining & Data Warehousing (CSPP 53015)
- Data-Intensive Computing Systems (CMSC 33520)
- Database Design & Implementation (MSCA 31005)
- Databases [Introduction to ] (CMSC 23500)
- Databases (MPCS 53001)
- Hadoop Workshop (MSCA 37001)
- High Performance Computing (CSPP 51087 / MPCS 51087)
- Networks & Distributed Systems (CMSC 33300)
- Scientific Parallel Computing (CMSC 34000)
- Data Visualization Techniques (MSCA 32007)
- Scientific Visualization (CMSC 33710)
- Tableau Workshop (MSCA 37005)
- Approximation Algorithms (CMSC 37503 / TTIC 31080)
- Complexity Theory [, Introduction to ] (CMSC 28100)
- Complexity Theory B (CMSC 38700)
- Computational & Metric Geometry (CMSC 39010 / TTIC 31100)
- Convex Optimization (CMSC 35470 / STAT 31015 / TTIC 31070)
- Mathematical Foundations (TTIC 31030)
- Mathematical Toolkit (CMSC 31150 / TTIC 31150)
- Numerical Methods (MPCS 58001)
- Health Analytics (MSCA 32006)
- Machine Learning for Public Policy (CAPP 30254)