Data Science, BS

Curriculum

The data science major leads to a bachelor of science. It requires 128 hours of coursework:

• BELL core requirements: 53 hours
• Major requirements (including 6 hours of tool courses and 12 hours of electives): 39 hours
• Minor requirements: 18 hours
• General electives: 15 hours

See All Program Requirements

Courses you'll take:

DSC 1010, Introduction to Data Science: This course will introduce students to this field and equip them with some of its basic tools as well as its general mindset. The focus in the treatment of these topics will be on breadth, rather than depth, with an emphasis on problem-based learning. Real data sets from a variety of disciplines will be used. Students who complete this course will learn the steps involved in data collection, exploratory data analysis, modeling, data visualization and effective communication.

MTH 1151, Elementary Statistics for the Sciences: The study of statistical procedures widely used in the sciences. Topics include modeling with probability distributions, multiple regression, analysis of variance, chi-square tests, nonparametric statistics and bootstrapping.

CSC 1110, Programming I: An introduction to computer organization, algorithm development and programming.

CSC 1120, Programming II: A continuation of algorithm development and programming, including basic aspects of string processing, recursion, internal search/sort methods and simple data structures.

DSC 1000, Seminar in Data Science: This course is required for all data science majors and is to be taken during the first spring semester after declaring Data Science as a major. The seminar provides an orientation to the field of data science and the study of data science at Belmont. Students will learn about data science coursework and curriculum, student organizations, research and other extracurricular opportunities for students, careers for data science graduates and graduate study in data science.

MTH 1210, Calculus I: An introduction to analytical geometry, limits, integration and differentiation.

MTH 1220, Calculus II: Further techniques of integration with applications; exponential and logarithmic functions; parametric equations; and sequences and infinite series.

DSC 2010, Statistical Computing: Students will learn to manipulate data and process information using an appropriate programming language (e.g. R, Python). No previous programming experience is assumed. Students will use data from multiple sources including experiments or surveys, local industry/non-profit partners and public data sets. Throughout the course students will analyze data using statistical and data science techniques. Students will become proficient at selecting the data of interest from large tabular sources, and applying sample aggregate functions (e.g. max, mean) as well as more complex statistical tests. Appropriate use of computers and software will be integrated into the laboratory and data analysis experience.

CSC 2020, Database Systems: An introduction to database management system concepts and applications. Students will practice the design and implementation of relational databases and use SQL to make accurate and efficient queries. Students will also work with unstructured, NoSQL databases, and learn the tradeoffs in efficiency and utility between different database paradigms. They will become proficient at accessing and manipulating data, through both direct, command-line interfaces and libraries embedded within more general programming languages.

DSC 4900, Data Science Project and Portfolio: This course provides students with an opportunity to make connections among ideas and experiences gained from foundations in mathematics, computer science and statistics, and apply them to another domain. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data, to applying suitable and appropriate analytics methods to the problem, and communicating the results in a clear and comprehensible way. The course will emphasize a good understanding of the foundational knowledge of the core discipline and the domain area and prepares students for future professional endeavors.

Students in Belmont’s Data Science program can enhance their classroom learning by pursuing data science jobs and internships (which are most often paid).

We also offer students opportunities to participate in a data scientist in residence program, data projects and industry office hours from the beginning of their studies to the time that they enter careers and/or graduate school.