BS in Data Science

A rapidly growing field providing students with exciting career paths and opportunities for advanced study, Data Science combines the computational and inferential ways of thinking and doing to enable the collection, exploration, and analysis of datasets for the purpose of identifying patterns, drawing conclusions, and making predictions about underlying, often-complex real-world processes. Data Science is inherently interdisciplinary given the diversity of disciplines needed to understand and model these processes, which may span natural, physical, social, economic, and humanistic dimensions.

The Data Science BS program in the Faculty of Computing & Data Sciences at Boston University is a rigorous program that covers the foundational as well as the applied dimensions of Data Science by focusing on aspects of mathematics, statistics, algorithmics, informatics, and software engineering that are relevant for analyzing and manipulating voluminous and/or complex data. To gain a deep appreciation of the human and social contexts, the regulatory and institutional structures, and the ethical and professional practices that shape technical work around computing and data science, the program equips students with the knowledge and skills needed to carry out the full cycle of data-driven investigative inquiry in real-world settings. The program is designed to provide students with ample opportunities to pursue a minor in another school or college in a discipline for which data-driven inquiry is prevalent—from natural, biomedical, social, and management sciences to arts and humanities.

The learning outcomes of the Data Science BS program are anchored in foundational, applied, integrative, and in-the-field training. As part of their foundational training, students develop mastery of the capabilities and limitations of the principal methodologies of data-driven, model-based prediction and decisionmaking. As part of their applied training, students develop the skills necessary to assemble computational pipelines and deliver reproducible data analysis of massive structured and unstructured datasets. As part of their integrative training, students develop the ability to assess the social impacts of data-centered methods, including adherence to policy, privacy, security, and ethical norms. As part of their in-the-field training, students leverage the skills and knowledge they acquired throughout the program to synthesize and complete a real-world capstone project curated through CDS Impact Labs and co-Labs, in collaboration with various internal and external partners.

Toward these objectives, the Data Science BS requires completion of at least 64 credits towards the major, including fourteen 4-credit courses covering the foundational, methodological, and applied dimensions of data science, as well as completion of a 4-credit capstone or practicum experience project—all completed with a grade of C or higher.

With this preparation, graduates from the Data Science BS program will be ready to contribute to the art, science, and engineering of the data-driven processes that are woven into all aspects of society, economy, and public discourse. They will be ready to pursue careers in which they contribute to the synthesis of knowledge through methodical, generalizable, and scalable extraction of insights from data, as well as to the design of new information systems and products that enable actionable use of those insights towards discovery and innovation in a wide range of application domains.

Learning Outcomes


Foundations consist of methods and skills deriving mainly from computer science, engineering, mathematics, and statistics. As part of their foundational training, students will develop the following:

  1. Mastery of the principal tools of data driven decisionmaking, including defining models, learning model parameters, and making predictions.
  2. Understanding of how to use dynamic and probabilistic models to make decisions.
  3. Understanding of the algorithmic principles behind data mining and machine learning, including optimization.
  4. Mastery of skills needed to manage and analyze massive structured and unstructured datasets.
  5. Mastery of skills needed to assemble computational pipelines and deliver reproducible data analysis.
  6. Ability to design experiments and identify data and features needed to test hypotheses and report outcomes.

Integration and Application

Integrative skills involve the application of data science to address domain-specific questions and the relationships between the data scientist with other fields and with broader society. “Domain” refers to a field of inquiry in which data science is being applied, e.g., marketing, medicine, literature, media, etc.

Through integrative learning, students will develop the following:

  1. Ability to apply foundational methods to address challenging problems in at least one domain.
  2. Ability to interpret and explain results, including developing narratives and data visualizations.
  3. Ability to assess the social impacts of data centered methods, including ethical considerations, fairness, and bias.
  4. Ability to understand and adhere to policy, privacy, security, and ethical norms.
  5. Ability to collaborate and communicate with teammates from domain disciplines, including the ability to deliver a significant (capstone) project focused on a particular domain.


All BU undergraduate students, including both entering first-year and transfer students, will pursue coursework in the BU Hub, the University’s general education program that is integrated into the entire undergraduate experience. BU Hub requirements can be satisfied in a number of ways, including coursework in and beyond the major as well as through cocurricular activities. Students majoring in Data Science will ordinarily, through coursework in the major, satisfy BU Hub requirements, the details of which are forthcoming. Students are advised to check the CDS website for more information regarding course substitutions and equivalencies, as well as GPA requirements for equivalent courses.

Mathematical & Computational Foundations: At least one course in each of the following subjects:

  • Introduction to DS: CDS DS 110 or equivalent
  • Foundations of DS I: CDS DS 120 or equivalent
  • Foundations of DS II: CDS DS 121 or equivalent
  • Foundations of DS III: CDS DS 122 or equivalent

Data Science Core: Six courses with at least one course in each of the six subjects listed below:

  • Programming: CDS DS 210 or equivalent
  • Data Mechanics: CDS DS 310 or equivalent
  • Statistics: CAS MA 213 or CAS MA 214 or equivalent
  • Algorithms: CDS DS 320 or equivalent
  • Machine Learning: CDS DS 340 or equivalent
  • Ethical & Social Implications: CDS DS 380 or equivalent

Data Science Electives: At least four courses with at least one course in each of the three subjects listed under either the methodology option or the in-the-field option.

  • Methodology Track:
    • Advanced DS Methods: CAS CS 531, CAS CS 565, CAS EC 524, CAS MA 416, CAS MA 589, or similar
    • Scalable & Trustworthy DS: CDS DS 563, CAS CS 528, CAS CS 561, CAS CS 562, CAS EC 528, or similar
    • Applied DS & AI: CAS CS 440, CAS CS 505, CAS EC 523, CAS MA 415, CAS MA 569, or similar
  • In-the-Field Track:
    • Analytics in the Field: CDS DS 549, CAS MA 415, QST QM 222, or similar
    • Algorithmics in the Field: CAS CS 506, CAS MA 569, CAS EC 527, ENG BE 562, QST BA 476, or similar
    • Data Science in the Field: CDS DS 519, CDS DS 537, CAS PO 399, CAS PO 599, or similar

Capstone Experience: At least 4 credits earned through completion of either of the following:

  • A supervised project pursued as part of a practicum course: CDS DS 499, CDS DS 519, CDS DS 539, CDS DS 549
  • A supervised project pursued as part of an internship/co-op or directed study course: CDS DS 490

Final 4.00 Credits: Can be fulfilled by any course that meets CDS requirements, including CDS DS 100.