This course provides students with the essential background in calculus and linear algebra needed to pursue the study of Data Science. Topics include derivatives and integrals of (multivariable) functions; vectors and matrices; vector spaces and subspaces; norms and projections; the eigendecomposition (diagonalization) of a matrix; the singular value decomposition (SVD) of a matrix; continuous optimization; mappings between Euclidean spaces; and Taylor approximation. Throughout, various applications to Data Science are considered, with hands-on numerical and coding exercises supplementing the theory.

This course covers Python programming fundamentals and database management systems. Students learn to design, implement, and query relational databases while developing proficiency in Python for data manipulation and analysis.

This course covers foundational principles that drive machine learning applications and provides practice implementing algorithms. Topics include maximum likelihood estimation, dimension reduction, supervised and unsupervised learning, neural networks, and non-parametric methods. Students gain tools to address new ML problems, applying techniques such as regression, SVMs, decision trees, clustering, and backpropagation, with an emphasis on practical problem-solving using software libraries and real data.

MA 521: This course provides a comprehensive foundation in statistical methods for data science. Topics include probability theory, statistical inference, hypothesis testing, and regression analysis.

FA 590: An advanced course focusing on statistical learning methods and their applications. Students explore modern statistical techniques for prediction and classification.

This course introduces deep learning concepts and methodologies, covering both theoretical foundations and practical applications. Topics include model selection, neural networks, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention models, and reinforcement learning from human feedback (RLHF). Students complete programming assignments and a final project involving design and implementation of deep learning models.

This course explores the management, governance, and strategic deployment of Artificial Intelligence (AI) systems within modern organizations. Students examine applications of AI across industries-such as autonomous transportation, fraud detection, and machine translation-while addressing managerial considerations like fairness, accountability, transparency, ethics, and legal compliance. The course emphasizes assessing and managing AI/ML systems, monitoring performance, and developing enterprise-level AI strategies.

This course explores the integration of augmented intelligence, generative AI, natural language processing, and deep learning in data-driven business contexts. Students gain foundational and practical knowledge in LLMs, prompt engineering, and AI ethics. A major team project involves developing a proof-of-concept business solution leveraging generative AI to create measurable value.

A culminating project where students apply their knowledge to solve real-world data science problems. Working individually or in teams, students complete an end-to-end data science project.

This course provides a foundational introduction to modern time series analysis from both theoretical and applied perspectives. Emphasizing the Box–Jenkins methodology, it covers ARMA and ARIMA models, parameter estimation, model diagnostics, forecasting, seasonal (SARMA) models, and time series models of heteroscedasticity (ARCH and GARCH). Students apply these methods using statistical software (R) and real-world datasets, developing skills to analyze, forecast, and interpret time-dependent data.

This course introduces the principles and practice of data mining and machine learning. Students explore statistical and computational techniques to analyze, classify, and model data. Topics include data preprocessing, decision trees, k-nearest neighbor algorithms, Naïve Bayes, clustering (k-means, hierarchical), regression, neural networks, and advanced methods such as boosting and recommendation systems. The course emphasizes both theoretical understanding and applied learning using Python and real-world datasets.

The field of Big Data is explored through both business and technical lenses. Students learn to manage the volume, velocity, and variety of data using modern technologies such as Spark, Python, and Dataiku. The course covers big data strategy, governance, AI ethics, and applications of machine learning and IoT within big data ecosystems, preparing students to design scalable solutions for enterprise environments.

This course focuses on the design, management, and use of data warehouse (DW) and business intelligence (BI) systems. The DW is the central element in collecting, integrating, and making sense of an organization's data. BI concerns the full range of analytical applications and their delivery to users. Students learn the business value of data, planning and requirements gathering, data architecture and modeling, and integration processes. Practical examples and case studies highlight the implementation of BI systems and data integration for improved organizational decision-making.

Students learn through hands-on experience how to extract and analyze data from the web using distributed computing. The course covers web scraping, text mining, recommender systems, clustering, and natural language processing. Students apply methods widely used by companies like Amazon and Google to analyze web-scale data, culminating in applications to real scientific or business questions.

This course develops students' analytical ability to understand consumer and customer behavior using marketing models, analytics, and data management techniques. Topics include customer analytics, product analytics, promotion and digital analytics, channel analytics, and marketing mix optimization. Students use SAS and Python tools to build realistic models, forecast consumer behavior, and formulate marketing strategies based on data-driven insights.

Effective visualization of complex data allows for meaningful insight and informed decision-making. This course investigates visualization methods from multiple perspectives and teaches students to use tools such as Tableau, Julius.ai, Python, R, and Power BI to refine data and communicate results effectively. Ethical visualization practices and design theory are also emphasized.

This course covers reinforcement learning theory and applications, including Markov decision processes, dynamic programming, temporal-difference learning, and deep reinforcement learning. Students implement algorithms and apply them to real-world problems.