Online MS in Data Science: Courses

This course introduces the Python programming language along with data analysis and exploration techniques. Topics covered include the fundamentals of Python programming, visualization, and exploratory data analysis using key libraries such as NumPy, Seaborn, Pandas, and Matplotlib.

This course provides a comprehensive understanding of fundamental statistical concepts that are essential for data analysis. The course explores basic statistical concepts including data modeling, random variables and hypothesis testing, clustering, principal component analysis, linear models, logistic regression, and analysis of variance. The course tackles common mistakes and issues in data analysis such as causality vs. correlation, confounders, statistical artifacts, Simpson’s paradox, base rate fallacy, stage migration, survivorship bias, censoring, and misleading visualization.

This course covers essential data science and the data processing pipeline including data capture (scraping, cleaning, and filtering), feature engineering (representation, selection, and transformation), data augmentation, data mining (predictive modeling), data visualization (search and exploration), and scalable computing and big data (Hadoop, Hive, and Spark). The course concludes with an introduction to supervised learning.

This course provides an overview of popular algorithms in machine learning. Topics include supervised and unsupervised learning, linear and polynomial regression, clustering, classification algorithms, gradient descent, support vector machines, decision trees, random forests, instance-based learning, neural networks, and genetic algorithms.

This course serves as an introduction to fundamental ethics concepts essential for the development of improved intelligent systems, while exploring their implications on the economy, civil society, and government. Topics include ethical data sourcing, data bias mitigation, explainability versus black-box AI, economic equity considerations, and data governance including privacy, security, and stewardship. Special focus is placed on the pivotal data and modeling choices made by developers during system creation and deployment, along with their societal ramifications.

This course entails an independent development, and documentation of substantial data science project using techniques and/or tools. The course includes periodic reporting of progress, plus a final oral presentation and written report.

This course introduces the R programming language for statistical computing and data analysis. Participants will learn the fundamentals of R programming, data manipulation, visualization, and statistical analysis techniques.

The course covers autoregressive, moving average, seasonal models, autocorrelation function, forecasting, spectrum, spectral estimators.

This course introduces the ETL pipeline: Extract, Transform, and Load. The course provides students with a technical overview on how to source, prepare, and manage data. Students also will be introduced to Dash and to the principles of NoSQL database systems.

This course covers the essential and practical skills necessary to communicate information about data clearly and effectively through graphical means based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course introduces the value of visualization, the principles and techniques of data visualization, and specific techniques in information and scientific visualization. The course also focuses on big data organization and mining for decision support, and how to best leverage visualization methods. The assignments will involve the use of Tableau, Seaborn, Dash, Folium, Matplotlib, and the ability to manipulate data sets with code.

The course covers data management and systems aspects of big data. Topics include an overview of big data management systems, distributed big-data storage, Programming models in big data, column-based storage, analytics on big data, big spatial data, and document databases.

This course covers principles of deep learning and in its applications. Students will learn how to build and use different kinds of deep neural networks using hands-on approach. Topics include feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers and encoders/decoders. The course will include hands-on applications covering natural language processing tasks, behavioral analysis, financial analysis and anomalies detection.

This course covers word vector representations, embeddings, syntax parsing, vector space modeling, dimensionality reduction, speech tagging, text classification, sentiment analysis, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks, and large language models.

This course covers the fundamentals of reinforcement learning using a problem-based approach by addressing goal-directed problems on automated learning in an uncertain environment. Topics include finite Markov decision processes, dynamic programming, Monte-Carlo simulations, temporal-difference learning including Q-learning, function approximation, and policy gradient methods.

The course covers the concepts, applications, algorithms, programming, and design of recommender systems. Topics include techniques for making non-personalized, content-based, and collaborative recommendations and their evaluation.

This course provides a theoretical foundation and practical skills for Generative AI. Topics include anatomy of generative models, transformers, GAN, the Diffusion Model for images. Students will be equipped with the skills to leverage state of the art techniques for visual representation, generative text, text to image synthesis and more.

This course examines real world examples of how insights gained through analytics to significantly improve a business or industry. Through our tour of real-world transformations driven by analytics, students will gain knowledge in the use of descriptive, diagnostic, predictive, and prescriptive analytics models.

The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.

This course covers statistical approaches in finance including building financial models, testing financial economics theory, simulating financial systems, volatility estimation, risk management, capital asset pricing, derivative pricing, portfolio allocation, proprietary trading, and portfolio and derivative hedging.

The course covers computational approaches for modeling uncertainty and solving decision problems. Topics include search techniques, constraint satisfaction problems, game playing (including alpha-beta pruning), propositional logic, predicate logic, knowledge representation and probabilistic reasoning. It also covers selected advanced topics in Artificial Intelligence.

This course addresses both the fundamentals and the research boundaries of algorithm design and analysis. Covered topics include: complexity of algorithms, divide and conquer techniques, greedy methods, dynamic programming, recursive backtracking, amortized analysis, graph algorithms, polynomial-time problem reduction, NP-completeness, approximation algorithms and a selected advanced topic.