Bachelor of Arts in Data Science

The Bachelor of Arts in Data Science is an interdisciplinary program that leverages the large quantities of data and computational resources that have become available over the last few decades to answer questions in a broad range of fields.

Degrees Offered

Bachelor of Arts

Program Types

Undergraduate

Mode of Study

On Campus

Department

Economics

School / College

College of Arts and Sciences

What is a data science degree?

The data science program offers students access to endless opportunities. This interdisciplinary degree is intended to extend beyond the STEM disciplines, helping you to develop both technical and interpersonal expertise. During the program, you will acquire essential data science skills such as data cleaning, visualization, statistical modeling, and machine learning. The technical skills you develop will be complemented and enhanced by interpersonal skills like 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.

Why should you major in data science at Redlands?

The data science program at Redlands is interdisciplinary, employing tools from mathematics, statistics, and computer science. The knowledge gained through this program can also be applied in areas that are not traditionally data-focused.

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.
During your final semester, you’ll engage in a capstone project tailored to both an area of interest and a specific job opportunity to complete a start-to-finish data science project that can be showcased to employers in your field of interest.

Make the most of a data science degree.

Work with your advisor to identify an application area. We highly suggest a second major or minor but an approved set of interdisciplinary courses that combine into an area of interest would also satisfy the application area. Choose elective courses to enhance your area of application. If you are planning to go on to graduate school in data science should consider a second major in economics, mathematics, or computer science.

Classes you'll take

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)

Completion requirements

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) – Students can choose from a wide range of Philosophy classes (PHIL) and should work with their advisor to choose one closely related to their application 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.

Undergraduate application deadlines

Fall 2025 - First Year

November 15, 2024 - Early Action

Fall 2025 - First Year

January 15, 2025 - Regular Decision*

Fall 2025 - Transfer

March 1, 2025 - Regular Decision*

Note

*Applications will be reviewed on a rolling basis after the deadline based on capacity.

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Find us on campus

Duke Hall 208

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What you’ll learn

Technical mastery

Develop core data science competencies including data cleaning, visualization, statistical modeling, and machine learning.

Strategic thinking

Cultivate non-technical skills such as critical thinking and creative problem-solving to pose significant questions and devise effective solutions.

Professional communication

Enhance communication skills to articulate complex data-driven insights clearly and persuasively, both in writing and verbally, to various audiences.

Project execution

Integrate technical and strategic skills through the execution of a full-scale data project relevant to your industry of interest.

What you’ll learn

Media card - Professor teaching students

Graduates pursue careers as

Data scientists

Data scientists are responsible for designing and implementing data models, analyzing and interpreting data, and communicating insights to stakeholders.

Data analysts

Data analysts are responsible for collecting, analyzing, and interpreting large sets of data to identify patterns and trends.

Machine learning engineers

Machine learning engineers are responsible for designing and building machine learning systems.

Business intelligence analysts

Business intelligence analysts are responsible for gathering and analyzing data to drive strategic decision-making.

Graduates pursue careers as

Mutiple media - Graduation ceremony for college of arts and sciences

Featured faculty

Every Redlands faculty member is an active practitioner in their field. The classes they teach emerge from their unique research and practices, and they’re passionate about what they’re sharing. At Redlands, faculty are invested in and committed to your success.