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 to have other essential, non-technical, skills such as intellectual curiosity, critical thinking, effective communication, creative problem solving, and the ability to formulate and revise meaningful questions within a discipline. Our program will encourage students to apply data science techniques to their individual fields of interest and motivate 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 the mathematics or the sciences but also from a broad range of humanistic and social science disciplines. We should support students from a wide range of backgrounds and with a wide range of 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. We should embody a welcoming community of scholars who build the confidence and agency of our students in their interaction with data.
• Our program should motivate students to do data science in the pursuit of good for the community and in the pursuit of humanistic, social, and scientific exploration.
• Our program should teach students to be good data stewards by ensuring that 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) CS 110 Introduction to Programming (offered every semester) |
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| Mathematical Foundation (one class) | DATA 100 Math for Data Science (offered every Spring) - In place of this class we would also accept one of: MATH 241 Linear Algebra, MATH 311 Probability, ECON 344 Mathematical Economics. |
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| Introduction to Data Science (one class) | DATA 101 Introduction to Data Science (offered every Fall) In place of this class we would also accept 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 webpage coming soon!) | |
| Database Management (one class) | DATA 211 Introduction to Database Management (offered every Spring, starting 2026) | |
| A course in Ethics (one class) | PHIL110, PHIL211, PHIL212, PHIL 213, PHIL 215, PHIL 216, PHIL 221, 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 (1 class) | DATA 401 Data Science Capstone Project Topic idea: (offered every Spring starting 2027) |
Your capstone project should be in the area of our field of application. |
Students should work closely with a Data Science faculty advisor to choose 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. The 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
What will my class schedule look like?
Year 1
Fall: First Year Seminar, Introduction to Data Science, +2 your choice
Spring: Introduction to Programming in Python, Introduction to Statistics, +2 your choice
Fall: Intermediate Data Science, A course in Ethics, +2 your choice
Spring: Mathematics for Data Science, +3 your choice
Fall: Data Science Elective, +3 your choice
Spring: Introduction to Database Management, +3 your choice
Fall: Data Science Elective, +3 your choice
Spring: 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 or Health Medicine and Society or 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 and 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 |