Master of Professional Studies: Data Science

UMBC’s Master of Professional Studies (M.P.S.) in Data Science program prepares students from a wide range of disciplinary backgrounds for careers in data science. In the core courses, students will get a fundamental understanding of data science through classes that highlight machine learning, data analysis and data management. The core courses will also introduce students to ethical and legal implications surrounding data science.

Beyond the core courses, students will take three courses in domain specific pathways developed in collaboration with academic departments across the university. Through these pathways, students will be able to utilize the skills and techniques they learned in the core courses within their own field or area of expertise.

Required Core Courses

DATA 601: Introduction to 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.

DATA 602: Introduction to Data Analysis and Machine Learning

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.

DATA 603: Platforms for Big Data Processing

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.

DATA 604: Data Management

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

DATA 605: Ethical and Legal Issues in 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

DATA 606: Capstone in 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.

DATA 607: Leadership in Data Science

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.

Prior to Spring 2024 ENMG 652 was accepted for this requirement.

Pathway Programs & Courses

The pathways will allow students who work in a particular domain to take classes specific to their industry. Each pathway will consist of three courses. See pathways at UMBC Main Campus, Shady Grove, and those that are available at both campuses.

UMBC Main Campus

Shady Grove

Programs at Both Campuses

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