Curriculum

Autumn 2022

41100 - Applied Regression Analysis – Max Farrell

BUS 41100 is a course about regression, a powerful and widely used data analysis technique. Students will learn how to use regression by analyzing a variety of real world problems. Heavy emphasis will be placed on analysis of actual datasets. Topics covered include: simple and multiple regression, prediction, variable selection, causal inference, residual diagnostics, classification (logistic regression), and time series (auto-regression), and introductory machine learning.

Winter 2023

32200 - Artificial Intelligence – Sendhil Mullainathan

It is hard to name a sector that will not be dramatically affected by artificial intelligence (or machine learning). There are many excellent courses that teach you the mechanics behind these innovations -- helping you develop an engineering skill set. This course takes a different approach. It is aimed at people who want to deploy these tools, either in business or policy, whether through start-ups or within a large organization. While this requires some knowledge of how these tools work, that is only a small part of the equation, just as knowing how an engine works is a small part of understanding how to drive. What is really needed is an understanding of what these tools do well, and what they do badly. This course focuses on giving you a functional, rather than mechanistic, understanding. By the end, you should be an expert at identifying ideal use-cases and thereby well-placed to create new products, businesses and policies that use artificial intelligence.

35126 - Quantitative Portfolio Management – Ralph Koijen

The course covers recent trends in quantitative investment strategies using new data based on, for instance, asset flows, portfolio holdings, and text sentiment (e.g. Twitter) and how these strategies are offered to investors via, for instance, ETFs or hedge funds. As part of the course, we will discuss modern data analytics tools and some basic Python programming. I will provide templates for all major strategies that will help you to develop and critically analyze your own investment ideas. The problem sets will familiarize you with Python, and quantitative investment strategies and big data analytics more broadly.

37304 - Digital and Algorithmic Marketing – Sanjog Misra

It should come as no surprise then that decisions in various marketing functions (including advertising, promotions, pricing and even product design) are now made based on or with the help of data and analytic algorithms. One could say that these algorithms are the marketer’s new competitive toolkit.

In this class we will explore the use of such algorithmic tools in furthering a firm’s digital (and non-digital) marketing goals. In particular, we will focus on methods to capture a consumer’s digital footprint and the algorithms used to use this data to tailor, improve and optimize the firm’s marketing investments. This course will require students to be conversant with digital technologies and somewhat comfortable with data and analytics although expertise is not required.

41206 - Decoding FinTech – Dacheng Xiu

This course provides a high-level introduction to two rapidly developing technologies: artificial intelligence and blockchain. Artificial intelligence, in particular machine learning and natural language processing algorithms, has been adopted by a variety of real-world FinTech companies that build their business based on credit scoring, fraud detection, real-estate valuation, portfolio management, and quantitative trading. Blockchain technology is the cornerstone of cryptocurrencies, smart contracts, and decentralized finance, a rising industry with great potential to disrupt the future of finance.

41917 - Causal Machine Learning – Max Farrell & Sanjog Misra

This course will bring students to the cutting edge in causal inference, giving them a solid theoretical understanding and ready-to-deploy tools for research. Using machine learning for estimation and inference of treatment effects has become an important part of modern academic economics. Students in this class will learn the theoretical underpinnings of this material as well as how to carefully and correctly apply the techniques in research. The course will prepare students for both theoretical and applied dissertation research. Each topic will be covered for two weeks, one covering theory and one covering application. Topics will include the basics of causal inference, nonparametric estimation, semiparametric inference, and double machine learning.

Spring 2023

37704 - Algorithmic Marketing Lab – Sanjog Misra

In this class we will explore the use of such algorithmic tools in furthering a firm’s digital (and non-digital) marketing goals. In particular, we will focus on methods to capture a consumer’s digital footprint and the algorithms used to use this data to tailor, improve and optimize the firm’s marketing investments. This course will require students to be conversant with digital technologies and somewhat comfortable with data and analytics although expertise is not required.

This course is a part of a two-course sequence. The first (37304) is an introduction to topics related to algorithmic marketing (the lab class (37704) is a more in-depth, hands on project based class that requires students to formulate and implement a working algorithmic product.) This class requires minimal coding apart from some exercises in Excel and running a few scripts in R.

37906 - Applied Bayesian Econometrics – Sanjog Misra

This course aims to be a slightly advanced introduction to applied Bayesian methods. The goal of the course is to introduce students to a variety of applications of the Bayesian paradigm. As such, an understanding of elementary Bayesian methods is assumed, although there will a review of basic concepts and methods in the first two weeks. Additionally, students are assumed to have taken undergraduate level Statistics, Calculus, Linear Algebra and have some familiarity with a programming language (preferably R, since all examples in class will use that.)

38921 - Tools for Thought – Sendhil Mullainathan

This course will explore "tools" that improve how we think. The word “tools” is used broadly to include mental software (frameworks and insights people may have had) as well as computational tools. The kind of thinking we will focus on is real world phenomena, ranging from how we behave in social settings to how we make judgments. Most of the class will focus on interventions that appear to change how people reason about their own minds and the minds of others (including cognitive behavioral therapy). It will be participatory and experiential: students will attempt to learn new tools of thought and apply them throughout the week between sessions. So at the end, students will have not just an abstract understanding of what we know about tools of thought, but an intimate one.

40912 - Healthcare Data for Researchers – Dan Adelman

In this course, Ph.D. students will work with national claims data from the Centers for Medicare & Medicare Services (CMS), using the CMS Limited Data Set housed at Booth for instructional purposes. Students will receive instruction on using and interpreting this data, and will conduct a major project to replicate a published study or complete a proposed project of their own. After completing this course, students will be well-prepared to conduct research using national claims data.

41201 - Big Data – Veronika Rockova

BUS 41201 is a course about data mining: the analysis, exploration, and simplification of large high-dimensional datasets. Students will learn how to model and interpret complicated `Big Data' and become adept at building powerful models for prediction and classification. Heavy emphasis is placed on analysis of actual datasets, and on development of application specific methodology. Among other examples, we will consider consumer database mining, internet and social media tracking, network analysis, and text mining.

41903 - Applied Econometrics – Christian Hansen

Some topics that may be covered are (i) heteroscedasticity and correlation robust inference methods including HAC, clustering, bootstrap methods, and randomization inference; (ii) causal inference methods including instrumental variables estimation, difference-in-differences estimation, and estimators of treatment effects under treatment effect heterogeneity; (iii) an introduction to nonparametric and high-dimensional statistical methods.