Data Science
The University of Redlands is building a new major in Data Science! Your experience of data science is highly tailored to your unique career goals.
Students in the Data Science Program will learn skills like data cleaning and preparation, data visualization techniques, statistical modeling, machine learning, and data management. However, these skills alone are meaningless without a driving data question that often comes from understanding fields outside of data science. Successful data scientists need other essential, non-technical skills, too — intellectual curiosity, critical thinking, effective communication, creative problem solving, and the ability to formulate and revise meaningful questions within a discipline.
These skills alone are meaningless without a driving data question.
Our program encourages students to apply data science techniques to their individual fields of interest, and motivates them to use data science in ethical ways that improve the world around them.
Program principles
- Our program should give students the foundational tools needed to do data science in the real world, along with the ability to build data science into their career paths and areas of academic interest.
- Our program should be inclusive. We should attract students not just from mathematics or the sciences, but also from a broad range of humanistic and social science disciplines — supporting students from a wide range of backgrounds and foundational skills.
- Our program should encourage diversity and allow for broad participation across groups of people who have not been traditionally encouraged to pursue data-type careers, building the confidence and agency of our students in their interaction with data.
- Our program should motivate students to do data science in pursuit of good for the community, and in pursuit of humanistic, social, and scientific exploration.
- Our program should teach students to be good data stewards, ensuring they understand how to ethically source, use, and share data.
Foundation courses
4 classes
| Requirement | Info | Notes |
|---|---|---|
| Statistics one class |
MATH 111 or POLI 202 or PSYC 250 | — |
| Programming one class |
GIS/DATA 167 Introduction to Programming in Python (offered every Spring) or CS 110 Introduction to Programming (offered every semester) |
— |
| Mathematical Foundation one class |
DATA 100 Math for Data Science (offered every Spring) | Also accepted: MATH 241 Linear Algebra, MATH 311 Probability, or ECON 344 Mathematical Economics. |
| Introduction to Data Science one class |
DATA 101 Introduction to Data Science (offered every Fall) | Also accepted: CS 211 Introduction to Data Science. |
Intermediate courses
3 classes
| Requirement | Info | Notes |
|---|---|---|
| Intermediate Data Science one class |
DATA 201 Intermediate Data Science (offered every Fall, starting 2025 — course page coming soon) | — |
| Database Management one class |
DATA 211 Introduction to Database Management (offered every Spring, starting 2026) | — |
| A course in Ethics one class |
PHIL 110, PHIL 211, PHIL 212, PHIL 213, PHIL 215, PHIL 216, or PHIL 221 | Other courses with a focus on ethics can be applied to the major by department permission. |
Capstone course
1 class
| Requirement | Info | Notes |
|---|---|---|
| Data Science Capstone Project one class |
DATA 401 Data Science Capstone Project (offered every Spring, starting 2027) | Your capstone project should be in the area of your field of application. |
Students should work closely with a Data Science faculty advisor to choose their remaining courses.
Elective courses
at least 2 classes
Electives should support an application area for your Data Science final project. We highly recommend a second major or minor in another field of application. Courses counted toward your application area must be at the 200 level or higher — we highly recommend students take Machine Learning.
- Questions? joanna_bieri@redlands.edu
Sample four-year schedule
| Year | Fall | Spring |
|---|---|---|
| Year 1 | First Year Seminar, Introduction to Data Science, +2 your choice | Introduction to Programming in Python, Introduction to Statistics, +2 your choice |
| Year 2 | Intermediate Data Science, a course in Ethics, +2 your choice | Mathematics for Data Science, +3 your choice |
| Year 3 | Data Science elective, +3 your choice | Introduction to Database Management, +3 your choice |
| Year 4 | Data Science elective, +3 your choice | Data Science Capstone, +3 your choice |
Example career pathways
| Data Science + area of emphasis | Example job listings |
|---|---|
| Data Science + Economics | Economic and Financial Analyst, Market Analyst, Marketing Scientist |
| Data Science + Mathematics or Physics | Masters in Data Science/Statistics, Machine Learning Engineer, Data Engineer |
| Data Science + Biology, Health Medicine & Society, Kinesiology, or Communication Science Disorders | Clinical Analyst, Healthcare Analyst, Informatics Nurse |
| Data Science + Business Admin or Global Business | Masters in Data Analytics, Business Intelligence Developer |
| Data Science + Accounting | Data Analytics Systems & Controls, Financial Technology |
| Data Science + English or Creative Writing | Data Journalist, Data Storyteller |
| Data Science + GIS or Environmental Science/Studies | Geospatial Data Scientist |
| Data Science + Studio Art | Video Game Design, Info-graphic Designer |