Enter the Emergent Field

of Data Science

Plan of Study & Courses

Advising | Plan of Study | Courses

Program Advising

Mentoring and advisement is an essential part of the program. Students meet with faculty and the program director to ensure that educational and vocational goals are being met. Prospective students are urged to consult with the program academic advisor Professor Amol Deshpande, via e-mail: amol@cs.umd.edu.

Plan of Study

The Data Science is a 12-credit (four 3-credit courses) program designed to be completed in 9 months of study. Students register for two courses (6 credits) in the fall semester and two courses (6 credits) in the spring. Visit Calendars & Deadlines to determine the semester schedule start and end dates as well as deadlines for cancellation or withdrawal. A minimum 3.0 GPA is required to maintain good academic progress for graduation. Students are responsible for keeping track of their progress and should review their academic record to ensure accuracy.

Course Title Offered
CMSC641 Principles of Data Science Fall
CMSC642 Big Data Systems
CMSC643 Machine Learning and Data Mining Spring
CMSC644 Algorithms for Data Science

Course Descriptions

CMSC641: Principles of Data Science, 3 credits. An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.

CMSC642: Big Data Systems, 3 credits. An overview of data management systems for performing data science on large volumes of data, including relational databases, and NoSQL systems. The topics covered include: different types of data management systems, their pros and cons, how and when to use those systems, and best practices for data modeling.

CMSC643: Machine Learning and Data Mining, 3 credits. Provides a broad overview of key machine learning and data mining algorithms, and how to apply those to very large datasets. Topics covered include linear models, classification techniques, Bayesian analysis, recommendation systems, and systems for large-scale machine learning.

CMSC644: Algorithms for Data Science, 3 credits. Provides an in-depth understanding of some of the key data structures and algorithms essential for advanced data science. Topics include random sampling, graph algorithms, network science, data streams, and optimization.

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