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Students trained in Data Science will study a blend of topics from many subdomains of communications, philosophy, mathematics, computer science, and information science. A data scientist has a breadth of experience across all of these fields but may not have as much knowledge as a specialist in any particular field. Furthermore, a data scientist trained at William & Mary is equipped to consider the philosophical and moral implications of algorithm development and data collection, and the societal ramifications that new approaches to data manipulation could have. This combination allows William & Mary Data Science students to (a) efficiently conduct computational analyses within their own knowledge domain, (b) manage teams of more specialized individuals to answer far-ranging questions, and (c) communicate technical findings to a wide variety of audiences. Individuals with this knowledge profile are revolutionizing a wide set of domains and are in very high demand not just by faculty researchers at William & Mary, but also by the public and private sectors.  

William & Mary offers a Bachelor of Science and a Minor in Data Science, which draw on faculty expertise from many departments. There are four key pedagogic pillars students will be expected to engage with during their time in the program: Computation, Application, Communication, and Deliberation.

• Computation - the computer science and mathematics required to responsibly use large datasets to create new knowledge. This is a focus of the introductory coursework, as well as the elective courses using more specialized data
• Application - the skills and creative thinking required to identify novel ways to apply computation to new problems. Application is present in all core courses within the Data Science program with goal to promote confidence and creativity.
• Communication - the written and oral skills to clearly transmit conclusions and implications derived from data analysis. While many students will naturally receive some communications training as a part of their time at William & Mary, the Data Science program promotes an additional depth of skill due to the challenges in communicating large sets of data. Communication is a strong theme within all Data Science core courses.
• Deliberation - the ability to consider the societal, moral, and ethical implications of Data Science. Students are required to take one course examining these topics, but many courses will integrate this type of thinking.

The B.S. in Data Science will require a minimum of 40 credits. The curriculum includes three tracks: Data Application, Algorithms, and Spatial Data Analytics. The degree program culminates in a capstone experience. Each track will further strengthen and deepen students’ understanding in data science.

The focus of the core curriculum is to provide students with a solid foundation in Data Science. Students learn data science theory and applications, including critical evaluation of how data can be used to solve novel problems, deliberation (considering the ethical, moral, and societal implications of data science), and communication. Through the core curriculum students learn the basics of programing, modeling, machine learning, data visualization, database structures, and ethics in data science. Students also will take one course in linear algebra and two courses in mathematical statistics. The curriculum provides opportunities for students to use their skills and knowledge to manage and analyze large data sets efficiently and effectively and to identify and answer novel questions in a variety of settings.

Students will choose a track area to gain knowledge, skills, and abilities that are more specific to particular career aspirations. They are required to take three courses from one of the following tracks: Data Application, Algorithms, or Spatial Data Analytics. Coursework for the Data Application track focuses on teaching additional skills (e.g., data with time dependencies) and providing a more in-depth understanding of analytical and data visualization tools commonly used by data scientists employed by the private industry or government. Coursework for the Algorithms track focuses on expanding students’ abilities to develop new software or algorithms for the ingestion or analysis of large sources of frequently near-real-time data. Coursework for the Spatial Data Analytics track focuses on integration of analytical and visualization tools that data scientists typically use when working with data that have spatial dependencies.

In the capstone experience, each student will work closely with a program faculty member to conduct a substantial research project that focuses on synthesis and critical analysis, problem solving in an applied and/or academic setting, creation of original material or original scholarship, and effective communication with diverse audiences.

Programs

• Data Science, BS
• Data Science, Minor

Courses

• DATA 100 - Critical Questions in Data Science
• DATA 141 - Programming for Data Science
• DATA 146 - Introduction to Data Science
• DATA 150 - First-Year Seminar
• DATA 202 - Ethics in Data Science
• DATA 211 - Data Visualization
• DATA 301 - Data Driven Decision Making
• DATA 310 - Applied Machine Learning
• DATA 311 - Databases
• DATA 330 - Applied Time Series Analysis
• DATA 340 - Topics in Data Science
• DATA 390 - Directed Research in Data Science
• DATA 410 - Advanced Applied Machine Learning
• DATA 424 - Mentored Data Science Teaching
• DATA 431 - Spatial Data Discovery
• DATA 440 - Special Topics in Data Science
• DATA 442 - Neural Networks & Deep Learning
• DATA 444 - Agent-Based Modeling: Simulating Human Development Processes from Neighborhood to Regional Scales
• DATA 444 - Agent-Based Modeling: Simulating Human Development Processes from Neighborhood to Regional Scales
• DATA 490 - Independent Research in Data Science
• DATA 498 - Internship

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