Master of Science in Data Science (MSDS)

CIP Code: 30.7001

Program Description:

The MSDS program focuses on exploring, processing, and analyzing large-scale data sources from the perspectives of computer science, data representation, data analytics, mathematics, and applied statistics. Students learn the theory and acquire practical, hands-on skills in algorithm development, software design & programming, data management, data mining, trend analysis, and data visualization. The program incorporates real-world applications of Data Science in various disciplines, such as artificial intelligence, computer vision, data-driven engineering, business intelligence, and the Internet of Things (IoT).

The curriculum provides training in software engineering and prepares the students for employment in computer software related areas, such as computer software design and development, and computer software applications in computer networks and Internet systems. After completing the undergraduate degree, a student is also prepared to enter an advanced degree program in a computer science related field if he/she desires.

Program Learning Outcomes:

Upon completion of the MS in Data Science program, the students will be able to:

  • Written & Oral Communication - Effectively communicate the results of data analysis to both technical and non-technical audiences.
  • Quantitative Reasoning & Creative Thinking - Collect, clean, and organize data from various sources and apply statistical and machine learning techniques to data.
  • Information Literacy - Demonstrate proficiency and resourcefulness in utilizing multiple sources of information to research, design, or implement solutions to problems.
  • Critical Thinking &  Problem-Solving - Apply critical thinking about data, identify patterns and trends, and solve problems using data analysis.
  • Specialized Knowledge &  Integrative Learning - Analyze and draw meaningful insights from complex datasets using advanced statistical and computational techniques.
  • Ethical Reasoning - – Identify and address ethical challenges related to data collection, privacy, bias in data analysis, and how to use data responsibly.

Background Preparation

Students admitted into the MSDS degree program are required to have a bachelor's degree (BS/BA/BE) in computer science/data science/engineering or in another field with a sufficient background in computer/data science and mathematics, including course work and/or experience equivalent to (as deemed appropriate by the Academic team) all the following subjects:

  • Mathematics – Calculus, Linear Algebra, and Statistics/Probability
  • Introduction to Python Programming Language and Programming Logic
  • Data Structures

Additional documents and/or an interview may be requested by the Academic team to assess and validate the qualification of an applicant who did not complete an undergraduate degree in Computer Science / Engineering.

A student who lacks any of the background preparation requirements listed above is expected to clear them by either (1) taking the course at SFBU or another approved institution/organization that is comparable in subject matter, quality, and rigor as SFBU and earning a grade of at least C or higher, or (2) taking and passing a proficiency exam on the subject. The student must clear background preparation requirements before acceptance to the MSDS program.

MSDS Curriculum

A minimum of 30 semester units of graduate study are required for the MSDS program. They include three foundation courses, four courses based on the student’s selection of specialization in Data Science, a required capstone course, and electives. The student also has the opportunity to choose elective courses outside of data science to broaden the student’s skillset.

The student must meet prerequisite requirements before enrolling in any course. Upon clearing background preparation work, the student starts to take courses to meet the degree requirements. The student must begin his/her graduate study with the subjects listed in the Foundation Requirements section.

I. Foundation Course Requirements (9 units - Required Subjects)

CS481G Introduction to Data Science
DS500 Mathematics and Statistics for Data Science
DS501 Python Programming for Data Science

II. Specialization Requirements (12 units)

The student is advised to consider industry trends and career choices when selecting data science courses. Before taking the Capstone Course near the end of the program, the student will have taken a minimum of 12 units of graduate-level courses shown below and 6 units of electives.

CS550 Machine Learning and Business Intelligence
CS570 Big Data Processing & Analytics
DS512 Data Engineering
DS520 Deep Learning
DS535 Large Language Models (LLM)
DS540 Natural Language Processing (NLP)
DS565 Generative AI-Driven Intelligent Apps Development
DS589 Special Topics (related to Data Science)

Selecting any four (4) courses from the above lists will meet the Specialization Course Requirements. Taking four (4) courses in a cluster area will also help the student develop desirable skills that support the chosen area of interest and profession.

III. Electives (6 units)

Students may select 6 units (a combination of 1, 2, or 3-unit courses) of subjects that earn graduate-level credits in Data Science or other majors to fulfill the elective requirement. When applicable, the student may take Curricular Practicum courses and engage in practical training to work on company projects that are directly related to the student’s course of study. CPT501 (part-time internship) and CPT502 (fulltime internship) courses, which earn one unit and two units, respectively, may be counted as elective courses. No more than 3 units of practicum coursework may be counted towards graduation.

IV. Capstone Course (3 units – Required Subject)

Upon completing all or most coursework for this program, the student is required to take the capstone course and, under the guidance of the course instructor, integrate the knowledge and skills learned from all of the courses taken during the program.

DS595 Data Science Capstone Course