Master of Professional Studies: Data Science

The goal of this class is to give students an introduction to and hands on experience with all phases of the data science process using real data and modern tools. Topics that will be covered include data formats, loading, and cleaning; data storage in relational and non-relational stores; data governance, data analysis using supervised and unsupervised learning using R and similar tools, and sound evaluation methods; data visualization; and scaling up with cluster computing, MapReduce, Hadoop, and Spark.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission.

This course provides a broad introduction to the practical side of machine-learning and data analysis. This course examines the end-to-end processing pipeline for extracting and identifying useful features that best represent data, a few of the most important machine algorithms, and evaluating their performance for modeling data. Topics covered include decision trees, logistic regression, linear discriminant analysis, linear and non-linear regression, basic functions, support vector machines, neural networks, Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design.

Prerequisite: DATA 601: Introduction to Data Science and enrollment in the Data Science program. Non-Data Science students may be permitted with instructor permission.

The goal of this course is to introduce methods, technologies, and computing platforms for performing data analysis at scale. Topics include the theory and techniques for data acquisition, cleansing, aggregation, management of large heterogeneous data collections, processing, information and knowledge extraction. Students are introduced to map-reduce, streaming, and external memory algorithms and their implementations using Hadoop and its eco-system (HBase, Hive, Pig and Spark). Students will gain practical experience in analyzing large existing databases.

Prerequisite: Enrollment in the Data Science program and DATA 601. Other students may be admitted with program director’s permission.

This course introduces students to the data management, storage and manipulation tools common in data science. Students will get an overview of relational database management systems and various NoSQL database technologies, and apply them to real scenarios. Topics include: ER and relational data models, storage and concurrency preliminaries, relational databases and SQL queries, NoSQL databases, and Data Governance.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission. Corequisite: DATA 601: Introduction to Data Science

This course provides a comprehensive overview of important legal and ethical issues pertaining to the full life cycle of data science. The student learns how to think through the ethics of making decisions and inferences based on data and how important cases and laws have shaped the data science field. Students will use real and hypothetical case studies across various domains to explore these issues.

Prerequisite: Enrollment in the Data Science program. Other students may be admitted with instructor permission. Corequisite: DATA 601: Introduction to Data Science

This is a semi-independent course that provides the advanced graduate student in the Data Science program the opportunity to apply the knowledge, skills and tools they’ve learned to a real-world data science project. Students will work with a real data set and go through the entire process of solving a real-world data science project. The project may be conducted with industry, government and academic partners, who can provide the data set, with guidance and feedback from the instructor.

Prerequisite: Completion of all other core courses.

In the rapidly evolving field of data science, technical expertise alone is not sufficient for success. Effective leadership is essential to navigate the complexities of data-driven decision-making and drive impactful outcomes. The course is designed as a practical stage-by-stage field guide for our students to their careers in data science. It provides valuable insights and strategies for individuals at different career stages, from aspiring data science tech leads to seasoned data science executives. Through a comprehensive examination of several case studies, students will develop a deep understanding of the leadership skills, capabilities, and virtues necessary for success in the field of data science.

Data science relies heavily on the principles of probability theory and inferential statistics for extracting meaningful insight from complex datasets. DATA 608 introduces students to the essential concepts and tools of probability theory and statistics that form the backbone of data-driven decision-making processes. The course emphasizes a combination of theoretical tools, and application-oriented analysis to enable students to utilize statistical methods effectively in real-world data science scenarios.

The contemporary literature on data science and machine learning primarily focuses on algorithms and methodologies and assume that the reader is already equipped with the relevant mathematics and statistics background. We have found students who want to delve into the foundations of basic machine learning and data science methods typically struggle with the required mathematical knowledge. This course brings the mathematical foundations of data science and machine learning to the fore and collects the necessary skillset in a single place so that the skills gap is minimized. The covered contents in this course consist of two major parts: The necessary and relevant concepts of linear algebra and the relevant calculus skills needed for solving optimization problems. For the linear algebra part, first an overview of the vector analysis, treatment of linear systems, matrices, subspaces, and determinants are presented. The main step in the linear algebra portion of the course deals with various matrix decomposition techniques. Specifically, the QR-decomposition, the LU-decomposition, the eigen decomposition, and the Singular Value decomposition are discussed in details and some of their applications (in data science and machine learning) are introduced. For the optimization portion of the course, first an overview of the necessary tools such as partial and directional derivatives, definition of critical points of multivariable functions, and the Hessian is provided. Then the Lagrange multiplier method for optimizations in the presence of constraints is introduced. At the end of the course, we will touch upon convex optimization and the gradient descent approach. If time permits, basic tools of the information theory will be discussed.

This course is designed to equip students with the essential skills to convey their findings in a compelling and accessible manner. Students will learn how to craft narratives that translate intricate data analyses into meaningful stories and engaging narratives for diverse audiences, including stakeholders, executives, and non-technical professionals.

This course reviews modern methods used in deep learning and neural network design from a practical perspective. Both a broad set of techniques that are commonly used in state-of-the-art neural network architectures and network styles prevalent in specific sub-domains like computer vision, natural language processing, and social network analysis are discussed. Students learn how to use these techniques in modern frameworks and how to apply these methods to new problems.

Prerequisite: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

This course aims to teach the use of natural language processing (NLP) as a set of methods for exploring and reasoning about text as data. The focus will be on the applied side of NLP. Students will use existing NLP methods and libraries in Python to textual problems. Topics include language modeling, text classification, sentiment analysis, summarization, and machine translation.

Prerequisites: Enrollment in the MPS DS program; DATA 602 or CMSC 478/678.

This course introduces Generative AI (GenAI) by focusing on practical applications and hands-on experience with cutting-edge GenAI models. Students will learn to implement and apply GenAI models to generate text, images, music, and videos while addressing the ethical challenges inherent in GenAI.

Prerequisites: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

This course provides a comprehensive introduction to applying data science techniques in the finance industry. It begins with an overview of financial data science and the various sources of financial data, moving through data cleaning, visualization, and analysis methods. Students will learn to apply supervised and unsupervised machine learning techniques, time series analysis, and natural language processing to real-world financial datasets. The course also covers essential financial models, portfolio optimization, anomaly detection, and algorithmic trading.

Prerequisites: Enrollment to MPS Data Program and DATA 602 or CMSC 678.

The special topics courses will cover emerging or specialized Data Science topics on an as-needed basis.

Supervision of student internship/co-op activities in the cybersecurity discipline. A short technical report that describes the activities conducted relevance to theoretical or operational concepts learned in other coursework and lessons gained through the internship/co-op experience is required at the end of the course. The course grade will be based on the technical report. The report will be submitted to the student’s Graduate Program Director by the last day of the semester.

Individualized research activities under faculty supervision related to data science.