Master’s of Professional Studies: Data Science

UMBC’s Master of Professional Studies (MPS) in Data Science program will prepare 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. Must be taken in first semester.

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

Prerequisite: DATA 601: Introduction to Data Science. Must be taken in first semester

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 will be conducted with industry, government and academic partners, who will be responsible for providing the data set, with guidance and feedback from the instructor.

Prerequisite: Completion of the required courses.

ENMG 652: Management Leadership and Communications

Students learn effective management and communication skills through case study-analysis, reading, class discussion and role-playing. The course covers topics such as effective listening, setting expectations, delegation, coaching, performance, evaluations, conflict management, and negotiation with senior management and managing with integrity.

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 Main Campus, Shady Grove, and those that are available at both campuses.

Main Campus

Advanced Computing & Analytics

In collaboration with the Department of Computer Science and Electrical Engineering

  • CMSC 615 Introduction to Systems Engineering
  • CMSC 625 Modeling and Simulation of Computer Systems
  • CMSC 627 Wearable Computing
  • CMSC 628 Mobile Computing
  • CMSC 636 Data Visualization
  • CMSC 653 Information and Coding Theory
  • CMSC 655 Numerical Computations
  • CMSC 661 Principles of Database Systems
  • CMSC 668 Service-Oriented Computing
  • CMSC 671 Principles of Artificial Intelligence
  • CMSC 673 Introduction to Natural Language Processing
  • CMSC 675 Introduction to Neural Networks
  • CMSC 676 Information Retrieval
  • CMSC 678 Machine Learning
  • CMSC 691 Special Topics in Computer Science (Permission from Graduate Program Director)
  • Any other relevant graduate-level course in Computer Science with permission from the Graduate Program Director.

Cybersecurity

In cooperation with the MPS Cybersecurity program

  • CYBR 620 Introduction to Cybersecurity
  • CYBR 650: Managing Cybersecurity Operations
  • CYBR 658: Risk Analysis and Compliance

Click here to learn more about the cybersecurity pathway.

Data Science Analysis

In collaboration with the Department of Information Systems:

  • IS 661 – Biomedical Informatics Applications
  • IS 706 – Interfaces For Info. Visualization & Retrieval
  • IS 707 – Applications of Intelligent Technologies
  • IS 721 – Semi-Structured Data Management
  • IS 722 – Systems and Information Integration
  • IS 728 – Online Communities
  • IS 731 – Electronic Commerce
  • IS 733 – Data Mining
  • IS 777 – Data Analytics for Statistical Learning
  • Other courses may also qualify. Please consult the Program Director.

Click here to learn more about the data science analysis pathway.

Economics/Econometrics

In collaboration with the Department of Economics:

  • PUBL 604 – Statistical Analysis
  • ECON 601 – Microeconomic Analysis
  • ECON 602 – Macroeconomic Analysis
  • ECON 611 – Advanced Econometric Analysis I
  • ECON 612 – Advanced Econometric Analysis II
  • ECON 652 – Economics of Health

Click here to learn more about the economics/econometrics pathway.

Healthcare Analytics

In cooperation with the Health IT program

  • HIT658: Health Informatics I
  • HIT759: Health Informatics II
  • HIT723: Public Health Informatics
  • HIT674: Process and Quality Improvement within Health IT
  • HIT751: Introduction to Healthcare Databases

Click here to read more about the healthcare analytics pathway.

Management Sciences

In collaboration with the College of Engineering and Information Technology:

  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)

Click here to read more about the management sciences pathway.

Policy Analysis

In collaboration with the Public Policy Department

  • ECON 600 Policy Consequences of Economic Analysis
  • PUBL 601 Political and Social Context of the Policymaking Process
  • PUBL 603 Theory and Practice of Policy Analysis
  • PUBL 607 Statistical Applications in Evaluation Research
  • PUBL 608 Applied Multivariate Regression Analysis
  • PUBL 610 (special topics)

Click here to read more about the policy analysis pathway.

Project Management

In collaboration with the College of Engineering and Information Technology:

  • ENMG 650: Project Management
  • ENMG 661: Leading Virtual/Global Teams
  • ENMG 663: Advanced Project Management Applications

Note: Students pursuing the Project Management pathway are eligible for a certificate in Project Management upon completion. Read more about the Project Management pathway here.

Shady Grove

Bioinformatics

Students can transfer in coursework from Foundation for Advanced Education in Science (FAES) at the National Institutes of Health (NIH) to serve as a nine-credit Bioinformatics pathway within the MPS. Within Bioinformatics, FAES offers three-credit courses, as well as one-credit and two-credit courses. The one and two credit courses will need to be combined to be considered as equivalents to three-credit graduate level courses offered at UMBC. See more at professional.umbc.edu/faes.

Cybersecurity

In cooperation with the MPS Cybersecurity program

  • CYBR 620 Introduction to Cybersecurity
  • CYBR 650: Managing Cybersecurity Operations
  • CYBR 658: Risk Analysis and Compliance

Click here to learn more about the cybersecurity pathway.

Management Sciences

In collaboration with the College of Engineering and Information Technology:

  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)

Click here to read more about the management sciences pathway.

Project Management

In collaboration with the College of Engineering and Information Technology:

  • ENMG 650: Project Management
  • ENMG 661: Leading Virtual/Global Teams
  • ENMG 663: Advanced Project Management Applications

Note: Students pursuing the Project Management pathway are eligible for a certificate in Project Management upon completion. Read more about the Project Management pathway here.

Spatial Analytics

In collaboration with the Department of Geography and Environmental Systems:

  • GES 770: Special Topics in GIS
  • GES 773: GIS Modeling
  • GES 774: Spatial Analysis
  • GES 778: Visualization and Presentation
  • GES 770: Spatial Processes with R
  • GES 775: Advanced Application Development: Python Geospatial Development
  • Read more about the Spatial Analytics pathway here.

Click here to read more about the spatial analytics pathway.

Programs at Both Campuses

Cybersecurity

In cooperation with the MPS Cybersecurity program

  • CYBR 620 Introduction to Cybersecurity
  • CYBR 650: Managing Cybersecurity Operations
  • CYBR 658: Risk Analysis and Compliance

Click here to learn more about the cybersecurity pathway.

Management Sciences

In collaboration with the College of Engineering and Information Technology:

  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)
  • Choose one of the ENMG courses (650-690)

Click here to read more about the management sciences pathway.

Project Management

In collaboration with the College of Engineering and Information Technology:

  • ENMG 650: Project Management
  • ENMG 661: Leading Virtual/Global Teams
  • ENMG 663: Advanced Project Management Applications

Note: Students pursuing the Project Management pathway are eligible for a certificate in Project Management upon completion. Read more about the Project Management pathway here.

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