Propel Your Career in the Data-Driven World

Pacific's MS in Data Science program is designed to equip students with the skills and knowledge required for a successful career in this in-demand field. The program features a flexible, hybrid course delivery tailored to working professionals and traditional students, spanning four semesters and 32 units. As a STEM-designated full-time program, it is CPT/OPT eligible for international students.

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Why a Master's in Data Science?

A master's degree in data science offers significant benefits for a successful career:

  1. High demand: Skilled data professionals are in demand across industries, ensuring diverse opportunities.
  2. Increased earnings: Enhance your earning potential as data scientists are among the highest-paid tech professionals.
  3. Future-proof career: Stay relevant in a rapidly evolving, data-driven world.
  4. Professional growth: Deepen your expertise and accelerate your career with advanced skills in data science.

Pursuing a master's in data science is a wise investment for long-term career success in today's data-driven world.

Data science students in San Francisco

Flexible and Hybrid Learning for Data Science Professionals

Our data science master's program is structured with working professionals and traditional students in mind, offering:

  • Hybrid course delivery for flexibility
  • Classes conducted outside of regular office hours
  • Recorded lectures for convenient access
  • Industry-sponsored capstone projects for real-world experience

Minor in Data Science and Dual Degree Options

  • Minor in Data Science: Gain sought-after skills and prepare for the MS in Data Science program. For more information, visit the Minor in Data Science webpage or contact DS minor advisor Dr. James Hetrick.
  • Dual Degrees in Five Years: Pursue our BS in Economics to Master of Science in Data Science pathway program and earn an undergraduate and graduate degree in just five years to expand your career opportunities.
Minor in Data Science
Economics to MS Data Science

  • A Bachelor's degree with a minimum GPA of 3.0 or higher
  • Must have coursework or experience in:
    • Linear algebra
    • Statistics
    • Programming in a high-level language (experience in R and Python preferred)

Applicants must submit:

  • Official transcripts for all college-level coursework
  • One (1) letter of recommendation
  • Resume
  • Statement of interest 

The statement of interest allows applicants to demonstrate their motivation, skills, and abilities that will contribute to their academic success in our program.

While there is no specific format required for this statement, applicants are advised to give particular consideration to:

  • Academic credentials
  • Experience in the foundational concepts :
    • Statistics or Linear Algebra
    • Computer programming (any language, but Python and R are preferred)
  • Commitment and personal stamina to undertake a fast paced, intensive academic program
  • Enthusiasm for this particular course of study

Neither the GRE nor the GMAT is required for admission to this program. 

  • Must have the U.S. equivalent of a 2.65 GPA to be eligible for admission to the program.
  • Must provide an official, course-by-course with GPA transcript evaluation for non-U.S. transcripts from one of the agencies accepted by the University.
  • Must provide official English proficiency test scores from either TOEFL or IELTS.


Documents should be sent to the following address:
University of the Pacific
Knoles Hall, Second Floor, Room 207B
3601 Pacific Avenue
Stockton, CA 95211-0110

Video Tour of the Program

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Learn from World-Class Professors and Industry Experts

Seasoned academic scholars and industry professionals teach our data science master's program from leading companies and research centers, including Google, PayPal and Charles Schwab.

Hands-On Experience with Major Bay Area Companies

Complete a capstone project that allows you to apply what you've learned and gain hands-on experience working on real projects with prominent Bay Area and Northern California companies.

Professor teaching a student in the computer lab