Introduction of mathematical theory underlying fundamental topics in machine learning. Topics include empirical risk minimization, optimization, regression, classification, and discrete probability.

Advanced mathematical theory underlying fundamental topics in machine learning. Topics include time complexity analysis, the analysis of recursive algorithms, graph theory, and graph search algorithms.

Introduction to storage and management of large-scale data using classical relational (SQL) systems. The course covers topics including the SQL data model and query language, relational data modeling and schema design, elements of cost-based query optimizations, relational data base architecture, and database-backed applications.

Implementation of the data science life-cycle and many of the fundamental principles and techniques of data science, including algorithms, statistics, machine learning, visualization, and data systems.

Introduces the principles of computing systems and infrastructure for scaling analytics to large datasets. Topics include distributed systems, memory hierarchy, model selection, heterogeneous datasets, and deployment at scale. Also discusses the design of systems such as MapReduce/Hadoop and Spark, in conjunction with their implementation.

Deep dive into concepts and methods that deal with accessing, managing, visualizing, and analyzing spatial data. Explores advanced data science concepts for spatial data, introducing principles and techniques of spatial data analysis, including geographic information systems, spatial big data management, and geostatistics.

Probabilistic models for knowledge representation and decision-making. Topics covered include graphical models, temporal models, and online learning, as well as applications to natural language processing, computational biology, adversarial learning, and robotics.

Introduction to machine learning which explores techniques for learning suitable representations from data. Topics include clustering, dimensionality reduction, manifold learning, principal component analysis, spectral embeddings, multilayer perceptrons, autoencoders, convolutional and recurrent neural networks, and other aspects of deep learning.

Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working recommender systems, and putting current ideas from machine learning research into practice.

Introduction to probability. Discrete and continuous random variables–binomial, Poisson and Gaussian distributions. Central limit theorem. Data analysis and inferential statistics: graphical techniques, confidence intervals, hypothesis tests, curve fitting.

An introduction to various quantitative methods and statistical techniques for analyzing data—in particular big data. Topics include basic inference, sampling, hypothesis testing, bootstrap methods, and regression and diagnostics. Offers conceptual explanation of techniques, along with opportunities to examine, implement, and practice them in real and simulated data. Taught in R.

Examines the greater context under which the practice of data science exists and explores concrete ways these issues surface in technical work. Students learn frameworks for understanding how individuals relate to social institutions, how to use them to identify how issues of fairness arise, and use them to propose and critique potential solutions.

Covers the principles, methods and applications of general accounting, cost accounting and investment ROI. Development of the three key financial statements and their interrelations, cost identification, product costing, inventory control, operational performance, and investment return.

Introduction to product markets, segmenting these markets, and targeting critical customers within segments. Analyzes strategies to position products and services within segments, the critical role of pricing, as well as product management, market research, promotion, selling, and customer support.

Covers debt and equity financing of the enterprise, the role of commercial banks, venture firms, and investment banks; along with enterprise valuation, economic value add, capital expenditure decisions, return on investment, cash flow management, and foreign currency translation.

Core business analytics concepts and skills including Excel, relational databases and Structured Query Language (SQL), principles of effective data visualizations and interactive data visualization (e.g., Tableau), and data preprocessing and regression analysis using data analytics programming (e.g., Python).

Addresses effective practices for management of business projects. Includes both project management processes—scheduling, milestone setting, resource allocation, budgeting, risk mitigation—and human capital management—communication, teamwork, leadership. Also considers requirements for effectively working across functional and organizational boundaries.

Examines the advantages and complications of the multinational organization with emphasis on translating marketing, financing, and operating plans in light of geographical, cultural, and legal differences across the globe.