Serving as an introduction course to the ADS program, ADS521 examines the history of data science, its status as a scientific and applied discipline in a modern-day world, and surveys all the important topics covered in the courses in the program and many of their applications to everyday life. Part of the course will also serve as review of mathematics and basic programming knowledge.

This course provides a comprehensive introduction to the fundamental concepts of statistics, equipping students with essential tools to interpret data and make informed decisions across diverse fields. Starting with an exploration of statistics as the language of data, the course delves into probability to quantify uncertainty and random variables to model real-world phenomena. Students will build confidence in their analyses through the Central Limit Theorem and learn to construct confidence intervals for sound predictions. The course progresses to hypothesis testing, enabling participants to separate meaningful signals from noise and identify significant trends. In the final weeks, students will focus on comparing groups, refining their ability to detect differences between populations and draw conclusions from complex datasets. Throughout the course, students will use Python effectively for statistical analysis, applying libraries such as pandas and NumPy to clean data, run tests, visualize results, and produce reproducible analyses. Through practical applications and hands-on learning, this course empowers learners to harness statistical methods and Python tools for real-world problem solving.

This course provides a comprehensive foundation in supervised machine learning, focusing on the development and application of predictive models. Students explore the mathematical theory and practical implementation of algorithms, including linear and logistic regression, decision trees, support vector machines, and ensemble methods such as Random Forests and Gradient Boosting. Through a blend of theory and applied computational practice in Python, students gain hands-on experience in the end-to-end modeling pipeline. Key topics include feature engineering, model selection, hyperparameter tuning, and the evaluation of model performance using bias-variance trade-offs. By the end of the course, students will be able to transform complex datasets into actionable predictions while ensuring model robustness and generalizability.

Building upon the foundations of supervised learning, this course transitions into advanced modeling techniques for discovery and complex pattern recognition. The first half of the course focuses on unsupervised learning, covering clustering algorithms (K-means, hierarchical), dimensionality reduction (PCA, t-SNE), and association rules. The second half provides an intensive introduction to Deep Learning, where students design and train artificial neural networks. Through guided coding labs and hands-on projects, students implement models using industry-standard deep learning frameworks. Topics include multi-layer perceptrons, convolutional neural networks, and recurrent neural networks. Students will gain the practical skills required to handle unstructured data such as text and images and acquire the necessary prerequisite knowledge for advanced agentic AI architectures.

This application-focused course focuses on regression analysis including linear, multiple linear and logistic regression models, with detailed discussions of model formulation, model inference, and model interpretation. Programming languages such as a SAS will be utilized.

This course is an introduction to data visualization. It includes data preprocessing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics ocvered include design principles, perception, color, statistical graphs, maps, trees and networks, data visualization tools, and other topics as appropriate. Visualization tools may include Tableau, Python, and R, etc. The course introduces the techniques necessary to successfully implement visualization projects using the programming languages studied.

It is increasingly important for data scientists to understand various database models and their associated data access methods. This course covers both the fundamental concepts of database systems and associated tools. Topics include conceptual data modeling, database design and normalization, database implementation and the use of SQL for data definition, manipulation, and query processing. The course also includes a survey of techniques for handling non-relational data models, massive datasets, and unstructured data, including data warehousing, in-memory databases, NewSQL, NoSQL, and Hadoop.

This course explores the fundamentals of Generative AI, its underlying architectures, practical applications, and ethical considerations. Students will gain hands-on experience in building and fine-tuning generative models, including Large Language Models (LLMs), image generators, and creative AI systems. After completing this course, students will: *Understand the core principles of Generative AI, including machine learning techniques, such as neural networks, GANs, and transformers. *Gain hands-on experience in training and fine-tuning generative models. *Apply Generative AI techniques to text, image, and multimodal data. *Develop awareness of ethical concerns related to generative AI. *Complete a capstone project demonstrating their knowledge.

Agentic AI & Skills: Intelligent Agent System Design introduces theoretical foundations and practical skills required to design, build, and evaluate intelligent agent systems. The course begins with the Transformer framework, which underpins modern foundation models, and progresses toward goal-driven, tool-using, memory-enabled agentic AI systems. The course is hands-on and skills-focused, walking students through real agentic examples each week rather than isolated prompt engineering. The course will compare contemporary open source agentic tools. Students will use one set of agentic AI tools and frameworks (including Vertex AI or other contemporary agentic platforms) to design, test, and refine intelligent agents that interact with tools, data sources, and environments. Each student will complete an Agentic AI & Skills Project, designing and implementing an end-to-end intelligent agent system that addresses a real-world business or operational problem. The course also covers evaluation, deployment considerations, risk management, and responsible AI governance, preparing students to apply agentic AI effectively and safely in organizational contexts.

This is a project-oriented course at the end of the program. Students will demonstrate their competence in the theory and practice learned from the program through the whole process of a complex data analysis project, including data collection, exploration, preparation, analysis, interpretation and presentation. The project can be either relevant to students' experience or aspired filed, accompanied by a final essay in whcih students reflect upon the goals of the program and their personal goals, demonstrate how they met these goals, and what work supports their arguments.