Data Science Courses

DSC602 Python for Data Science

[3–0, 3 cr.]

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.

DSC603 R Programming

[1–0, 1 cr.]

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.

DSC604 Statistics for Data Science

[2–0, 2 cr.]

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 and interpretation of results.

DSC605 Time Series Analysis

[1–0, 1 cr.]

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

Pre-requisite: DSC602 Python for Data Science 

DSC610 Data Science and its Applications

[3–0, 3 cr.]

This course covers essential data science and the data processing pipeline including data capture (scraping, cleaning, wrangling, 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.

DSC611 Applied Machine Learning

[3–0, 3 cr.]

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.

Pre-requisite: DSC604 Statistics for Data Science 

DSC612 Data Ethics

[1–0, 1 cr.]

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 and algorithm 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.

DSC613 Data Engineering

[1–0, 1 cr.]

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.

Co-requisite: DSC610 Data Science and its Applications 

DSC614 Data Visualization

[2–0, 2 cr.]

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.

DSC615 Big Data Analytics

[2–0, 2 cr.]

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.

Pre-requisite: DSC602 Python for Data Science and DSC604 Statistics for Data Science

DSC622 Deep Learning and its Applications

[3–0, 3 cr.]

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.

Pre-requisite: DSC602 Python for Data Science and DSC605 Time Series Analysis 

DSC623 Natural Language Processing with Deep Learning

[2–0, 2 cr.]

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.

Pre-requisite: DSC602 Python for Data Science

DSC624 Reinforcement Learning

[2–0, 2 cr.]

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.

Pre-requisite: DSC602 Python for Data Science 

DSC625 Introduction to Generative AI

[2–0, 2 cr.]

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.

Pre-requisite: DSC622

DSC626 Recommender Systems

[1–0, 1 cr.]

DSC641 Business Analytics for Competitive Advantage

[3–0, 3 cr.]

DSC642 Analytics Applications

[3–0, 3 cr.]

DSC643 Statistical Methods on Finance

[3–0, 3 cr.]

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.

Pre-requisite: DSC604 Python for Data Science

DSC651 Design and Analysis of Algorithms

[3–0, 3 cr.]

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.

DSC652 Artificial Intelligence for Managers

[3–0, 3 cr.]

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.

DSC697 Research Methods in Data Science

[3–0, 3 cr.]

DSC698 Capstone Project

[3–0, 3 cr.]

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.

DSC699 Thesis

[6–0, 6 cr.]

This course entails an independent investigation of a topic of interest, in a basic or applied data science area, with the objective of producing original results.