{"id":85073,"date":"2021-05-03T11:42:25","date_gmt":"2021-05-03T15:42:25","guid":{"rendered":"https:\/\/www.bu.edu\/academics\/?page_id=85073"},"modified":"2026-04-29T23:02:19","modified_gmt":"2026-04-30T03:02:19","slug":"bs-in-data-science","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/academics\/cds\/programs\/bs-in-data-science\/","title":{"rendered":"BS in Data Science"},"content":{"rendered":"
\n

Contact<\/h3>\n

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences Advising website<\/a>.<\/p>\n<\/div>\n

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.<\/p>\n

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\u2014from natural, biomedical, social, and management sciences to arts and humanities.<\/p>\n

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.<\/p>\n

Toward these objectives, the Data Science BS requires completion of at least 64 units toward the major, including fourteen 4-unit courses covering the foundational, methodological, and applied dimensions of data science, as well as completion of a 4-unit capstone or practicum experience project\u2014all completed with a grade of C or higher.<\/p>\n

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 toward discovery and innovation in a wide range of application domains.<\/p>\n

Learning Outcomes<\/h2>\n

Foundations<\/h3>\n

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:<\/p>\n

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

    Integration and Application<\/h3>\n

    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. \u201cDomain\u201d refers to a field of inquiry in which data science is being applied, e.g., marketing, medicine, linguistics, media, etc.<\/p>\n

    Through integrative learning, students will develop the following:<\/p>\n

      \n
    1. Ability to apply foundational methods to address challenging problems in at least one domain.<\/li>\n
    2. Ability to interpret and explain results, including developing narratives and data visualizations.<\/li>\n
    3. Ability to assess the social impacts of data centered methods, including ethical considerations, fairness, and bias.<\/li>\n
    4. Ability to understand and adhere to policy, privacy, security, and ethical norms.<\/li>\n
    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.<\/li>\n<\/ol>\n

      Requirements<\/h2>\n

      All BU undergraduate students, including both entering first-year and transfer students, will pursue coursework in the BU Hub,\u00a0the University’s\u00a0general education program that is integrated into the entire undergraduate experience. BU Hub requirements<\/a> 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. Students are advised to check the CDS website for more information regarding course substitutions and equivalencies, as well as GPA requirements for equivalent courses.<\/p>\n

      Toward these objectives, the Data Science BS requires completion of at least 16 courses toward the major\u2014including 14 courses covering the foundational, methodological, and applied dimensions of data science, as well as completion of a capstone or practicum experience project course\u2014all completed with a grade of C or higher.<\/p>\n

      Mathematical & Computational Foundations:<\/strong> At least one course in each of the following subjects:<\/p>\n