Bachelor of Arts in Data Science

Data Science

From the traditional STEM disciplines to broader pathways like esports, healthcare, English, political, and environmental sciences, the data science program offers students access to endless opportunities.

Data science is a fundamentally interdisciplinary field that leverages the large quantities of data and computational resources that have become available over the last few decades, allowing questions to be answered in a broad range of fields.

The Data Science Program is intended to extend beyond the STEM disciplines and transcend our institution’s and program boundaries. If you are pre-med, data science can be utilized in projects involving medical imaging, epidemics and disease spread, pharmacology, and healthcare analytics. If your passions lie in political or environmental science, you can utilize data to effect change with data-backed policies, research, and decision-making. In the arts, data science and AI can be employed to detect fraud or explore and create new artistic products. If you are a video gamer or part of our Esports program, you can learn how AI is utilized to enhance the gaming experience.

Although data science employs tools from mathematics, statistics, and computer science, the knowledge gained can also be applied in areas that are not traditionally data-focused. If you are late in deciding on a STEM career, have never taken pre-calculus or trigonometry, or lack fluency with computer programs, you still have an opportunity for success in the Data Science Program, where you will be both challenged and supported.

During the program, you will acquire skills such as data cleaning, visualization, statistical modeling, and machine learning—skills essential in the data science field. These technical skills must be complemented by non-technical skills, such as critical thinking, effective communication, and creative problem-solving. You will learn to pose meaningful questions and find solutions that will impact your career and your community. Engaging in a data science capstone project tailored to both an area of interest and a specific job opportunity, you will complete a start-to-finish data science project that can be showcased to employers in your field of interest.


Data science jobs are dynamic and filled with innovation and opportunities. Industries are on the lookout for qualified individuals who have mastered skills like data analysis, data communication, problem-solving, prediction, modeling, and leadership. The Data Science program at the University of Redlands will give you both the technical and non-technical skills that will translate into future-focused career opportunities.

Career pathways are plentiful, including accounting, financial technology, business and marketing analytics, healthcare analytics, environmental analytics, data engineering, data visualization, data journalism, and more. In fact, the Bureau of Labor Statistics predicts that Data Science jobs will grow by 35% between 2022 and 2032.



Joanna Bieri (Economics – full-time – program director)

Nathaniel Cline (Economics – full-time)

Nicholas Reksten (Economics – full-time)

Cheyne Murray (ITS – part-time instructor)



Click here for our major planning worksheet:

The Bachelor of Arts in Data Science consists of 40 credits in which students will explore courses to satisfy the key competencies as outlined by the American Statistical Association's Curriculum Guidelines for Programs in Data Science while also exploring application areas found throughout the Liberal Arts. Skill areas include:

  • Computational and statistical thinking (CST)
  • Mathematical foundations (MF)
  • Model building and assessment (MB)
  • Algorithms and software foundations (ASF)
  • Data curation (DC)
  • Knowledge transference, communication and responsibility (KT)

Foundation Courses (4 courses – 16 credits):

  • Introduction to Statistics (4 Credits) – (CST/MF) – MATH 111 or POLY 202 or PSYC 205
  • Introduction to Programming (Python, R or Java) (4 Credits) –(CST/ASF) – DATA/GIS 167 or CS 110
  • Math for Data Science (4 Credits) (MF) – DATA 100 or credit for MATH 221 and MATH 311
  • Introduction to Data Science (4 Credits) (MB/ASF/DC/KT) – DATA 101 - This course is the first in a sequence in which students develop an application area to build into a capstone project.

Intermediate Courses (3 courses – 12 credits):

  • Introduction to Data Science II (4 Credits) (MB/ASF/DC/KT) – DATA 201 * This course is the second in a sequence in which students develop an application area to build into a capstone project.
  • A course in Ethics (4 Credits) – (KT) – Studnets can choose from a wide range of Philosopy classes (PHIL) and should work with their advisor to choose one closely related to their applicatoin area.
  • Database Management (4 Credits) (ASF/DC) – DATA 330

Elective Courses Application Area (2 courses – 8 credits) 200 level or higher at least one at the 300 level:

  • Students should work closely with their Data Science advisor to choose electives courses that support their application area. The are currently more than 30 classes to choose from

Capstone Project (1 course – 4 credits):

  • Data Science Capstone (4 credits) (MB/DC/KT) – DATA 401 – This capstone course requires students to integrate their knowledge of data science including data processing and cleaning, exploratory data analysis, visualization, prediction, privacy, and ethics and to apply this knowledge to their application area. This course is the third and final course in a sequence in which students develop an application area to build into a capstone project. By the end of this course students should have a fully published body of work surrounding the application of data science to their application area.

Application area:

  • Students should work with their advisor to identify an application area. We highly suggest a second major or minor but an approved set of interdisciplinary courses that combine into a area of interest would also satisfy the application area. Students should choose elective courses to enhance their area of application. Students planning to go on to graduate school in Data Science should consider a second major in economics, mathematics or computer science.