Structure & Interpretation of Computer Programs

An introduction to programming and computer science focused on abstraction techniques for managing program complexity.

Techniques include procedural abstraction; control abstraction using recursion, higher-order functions, generators, and streams; data abstraction using interfaces, objects, classes, and generic operators; and language abstraction using interpreters and macros.

The course exposes students to programming paradigms, including functional, object-oriented, and declarative approaches.

Linear Algebra & Differential Equations

Basic linear algebra; matrix arithmetic and determinants.

Vector spaces; inner product spaces.

Eigenvalues and eigenvectors; orthogonality, symmetric matrices.

Linear second-order differential equations; first-order systems with constant coefficients. Fourier series.

Financial Economics

This course focuses on the analysis of financial assets and institutions.


It emphasizes modern asset valuation theory and the role of financial intermediaries and their regulation in the financial system.

Psychology & Economics

This course presents psychological and experimental economics research demonstrating departures from perfect rationality, self-interest, and other classical assumptions of economics and explores ways that these departures can be mathematically modeled and incorporated into mainstream positive and normative economics.

It focuses on the behavioral evidence itself, especially on specific formal assumptions that capture the findings in a way that can be incorporated into economics.

Microeconomics

This course introduces students to the main tools and concepts of microeconomics.


Topics covered include consumer theory, producer theory, equilibrium in a competitive market, monopoly, general equilibrium, and asymmetric information.

Macroeconomics

This course introduces students to the main approaches economists use to describe how the economy works at the aggregate level.


Topics covered include economic growth, business cycles, the determinants of aggregate employment, unemployment, and inflation, and the effects of monetary and fiscal policy.

Econometrics

This course provides an introduction to statistical and estimation analysis of economic data.


It covers topics such as the linear regression model and its estimator, Ordinary Least Squares, as well as extensions such as Instrumental Variables models, panel data models, and time series models.

Data Structures

Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays, strings, and hash tables.

Storage management. Elementary principles of software engineering. Abstract data types.

Algorithms for sorting and searching. Introduction to the Java programming language.

Probability & Machine Learning for Data Science

An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems.

Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods.

Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics.

Applied Data Science w/ Venture Applications

This course surveys various concepts that are useful for designing and building data science, AI, and Machine Learning applications and systems.

Mathematical concepts include filtering, prediction, classification, decision-making, LTI systems, spectral analysis, & frameworks for learning from data.

Each math concept is implemented using Python libraries like NumPy, Pandas, Scikit-learn (ML modeling), Tensorflow & Keras (deep learning), and many other topics related to NLP, Neural Networks, Recommender Systems, etc.

Advanced Business Analytics

Business decision modeling, exploratory data analysis, cluster modeling, predictive modeling

Introduction to R and Jupyter software for data analysis; Real-world and real-data business practicum across a variety of industries

Topics include: Classifier cross-validation, KNNs, logistic regression, decision tree, naive Bayes, support vector machine, neural networks, multinomial classification, classifier tuning, cluster evaluation, hierarchical agglomeration, k-means, Gaussian mixtures, etc.

Corporate Financial Statement Analysis

This course covers the principles and practice of business finance, specifically project evaluation, capital structure, and corporate governance.


Firms' policies toward debt, equity, and dividends are explored in addition to the incentives and conflicts facing managers and owners.