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2025-2026 Undergraduate Bulletin
Data Science and Machine Learning, BS Major in
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Program Description
The program prepares students for careers in data science, quantitative research, machine learning, and data analytics. The curriculum includes the mathematical and algorithmic and software knowledge and skills used in different aspects of data science, such as data analysis, modeling, and visualization, statistical analysis and inference, and machine learning. The program provides a strong foundation in mathematical methods used in data science, including linear algebra, and statistical inference as well as a wide range of computer science applied topics relevant to data science such as deep learning, computational finance, and AI. Students will also be exposed to data and data science problems in an additional discipline such as biology, economics, chemistry, neuroscience, linguistics, or others. Students will develop strong data analytics and computational skills. The program culminates with a capstone project in which students will apply their mathematics, data science and machine learning skills on a large dataset.
Program Objectives
The graduates in B.S. in Data Science and Machine Learning will achieve the following program educational objectives:
- Become successful practicing data scientists who contribute professionally and ethically to society and make impactful contributions to their industry.
- Pursue graduate studies and attain a graduate degree in data science or a related field.
- Pursue professional development through lifelong learning, continuing education, and participation in professional organizations.
Student Outcomes:
- Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply theory, techniques, and tools throughout the data science lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
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Program Requirements
Candidates for graduation must fulfill the following requirements as well as the general BS degree requirements. 1. The Successful Completion of at Least 124 Semester Hours The successful completion of at least 124 semester hours and a cumulative grade point average of 2.0 in work completed at Hofstra. 2. Liberal Arts At least 62 semester hours must be in liberal arts courses. 3. Residence Requirements There are two requirements that must ordinarily be completed in residence at Hofstra: 15 semester hours in the major field of specialization and the last 30 semester hours. The 15 semester hours need not be included within the last 30 hours. General Requirements
The following courses and requirements must be successfully completed. - WSC 001 - Composition Semester Hours: 3-4
or placement examination (footnote 1) and - WSC 002 - Composition Semester Hours: 3
6 semester hours in Humanities distribution
6 semester hours in Social Sciences distribution.
Students transferring in with previous social science/humanities credits may use them in place of requirements in the same category as the transferred credits. Students may not take courses on a Pass/D+/D/Fail basis.
Natural Science Requirements
One two-semester sequence of science courses with laboratories (PHYS 011A & 011B and 012A & 012B, or CHEM 003A & 003B and 004A & 004B, or BIOL 112 and 113): Major Courses
The following major courses are required for a total of 57 s.h: Capstone Project Course in Data Science
A 3 s.h. capstone project course in Data Science. Take one of the following courses under a faculty’s advisement: Technical Electives
And 12 s.h. in technical electives from the list below or another course with permission from the program director: Additional Electives in a Data Intensive Discipline
Take at minimum of 6 additional credits in at least two upper level classes for majors from one of the following disciplines or related ones. Courses must cover data sources, characteristics, and data inspired questions and problems. Courses must be approved by the program director based on the syllabus. Psychology, Biology, Geology, Physics, Chemistry, Geography, Health-Sciences, Linguistics, Cognitive Science, Neuroscience, Economics, Finance, Business/Marketing, Engineering, Sociology, Political Science, History. No courses in this category can overlap with any Distribution or Natural Science requirements. Students could use Distribution or Natural science courses as prerequisites for upper courses in any of these areas. A grade of C- or Better
A grade of C- or better is required in all courses, with the exception of one course where a D or D+ grade is accepted (a waiver from the department is required). An Overall Average of C or Better
An overall average of C or better is required in CSC 014 , CSC 015 , and CSC 016 for continuation in the major. In addition, a student may not take any of these three courses more than twice. Footnotes
1. If this requirement is fulfilled by passing the placement examination, 6 semester hours in the humanities or social sciences should be taken with advisor’s approval. 2. Students taking BIOL 112 and BIOL 113 as part of their Natural Science requirements must take BIOL 111 first if high school GPA < 3.4 and must take CHEM 003A and BIOL 112 before taking BIOL 113 . The sequence of three courses, BIOL 112 , CHEM 003A , and BIOL 113 , will fulfill the Natural Science requirements. Sample 4 Year B.S. Data Science and Machine Learning
| Senior Fall | 16 | Senior Spring | 16 | | Technical elective/Additional discipline | 3 | Technical elective/Additional discipline | 3 | | Humanities/Social Science Distribution | 3 | Capstone Project | 3 | | Free elective | 10 | Humanities/Social Science Distribution | 3 | | | | Free elective | 7 | |
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