Data Science, Certificate

Year 1

Fall Semester (6 credits)

• DATA 5100 - Foundations of Data Science
• DATA 5210 - Statistical Inference and Modeling

Spring Semester (3 credits)

• CPSC 5331 - Data Management and Analytics

REQUIRED

• Application: Completed application for graduate admission. No fee to apply. 
• Transcripts: Official transcripts of all post-secondary education institutions attended in the last 90-quarter/60-semester credits of the bachelor’s degree, including any transfer credits earned during this time and any post-baccalaureate coursework. Unofficial transcripts can be used for the admissions process if official transcripts are unavailable.
• Academic Background: Successful applicants can come from all backgrounds but have completed the undergraduate courses listed below. See descriptions in the course catalog. See options for completing prerequisite coursework in the next dropdown.
  • Elementary Probability and Statistics (such as MATH 2310)
  • Programming course in Python (such as CPSC 1220)
  • Integral Calculus (such as MATH 1335)
• Minimum of 3.00 GPA: For information on GPA calculations for admission, please visit the Graduate Admissions How to Apply page. Applicants with less than a 3.00 GPA might be considered for probationary admission and can submit an additional GPA explanation statement.
• Resume or CV
• GRE: The GRE is not required for most students. Students subject to the Three-Year Baccalaureate Degree policy or who have earned degrees from institutions issuing non-graded transcripts must submit official results from the GRE (Code 4695) or GMAT (Code 5613) to be considered.
• Deposit: Upon admission, this program requires a non-refundable $250 deposit to confirm your space. The deposit will be applied toward your first semester tuition bill.

OPTIONAL

• Personal Statement: A 300-word essay on one of the following topics: the job you would like to have after graduating OR a problem you would like to solve, or have already solved, using analytical skills.

International Students: International students may be required to submit additional materials in order to have their applications evaluated. If English is not the applicant’s native language, a demonstration of English proficiency meeting the university’s entrance requirements must be submitted. Please visit the International Admissions page for complete information.

No Application Fee, Ever

No application fee: We are committed to removing barriers to graduate education by waiving the application fee for all applicants. Don't wait to get started—apply now.

If you haven’t completed all the required courses for entry into the Graduate Certificate in Data Science, don’t worry. We offer flexible ways to satisfy prerequisite coursework—through Seattle University, local community colleges or online courses—so you can start your graduate journey with confidence. 

Elementary Probability and Statistics Requirement:

To satisfy the Elementary Probability and Statistics prerequisite, courses should cover probability, confidence intervals, hypothesis testing and regression. Calculus-based courses are strongly recommended.

Seattle University Courses

• CRJS 3020 – Criminal Justice Statistics
• ECON 2100 – Business Statistics
• EVST 3400 – Research Design and Statistics
• MATH 1210 – Statistics for Life Sciences
• MATH 2310 – Probability and Statistics for the Sciences and Engineering*
• MATH 3412 – Mathematical Statistics
• PSYC 3050 – Statistics and Research Methods II
• PUBA 4400 – Research Design and Statistics
• SOCW 4010 – Critical Research Literacy for Social Work

*Course offered online in the summer.

Learn more about these courses in the Seattle University course catalog. 

Community College Courses

If you’d like to complete this requirement through another institution, your coursework must transfer as one of the following Seattle University equivalents. Refer to the Transfer Equivalency Guide to explore your options:

• MATH 1210 – Statistics for Life Sciences
• MATH 2310 – Probability and Statistics for the Sciences and Engineering
• ECON 2100 – Business Statistics

Programming Course in Python Requirement:

The Python prerequisite can be satisfied in one of three ways:

1. University Course
   Complete a university-level course in computer programming using Python. You will need to provide a transcript showing successful completion. If “Python” is not in the course title, please include a syllabus or other documentation demonstrating that Python was the primary language used.
2. Approved Online Course
   Complete one of the following online courses and provide documentation of successful completion:
   • Crash Course in Python (Free) – offered by Google on Coursera* at no cost
     *Must complete the Object-Oriented Programming (OOP) module listed as optional
   • Introduction to Computer Science and Programming Using Python (Free) – offered by MIT on EdX at no cost
   • Professional Certificate in Python Data Science – offered by IBM on EdX
3. Python Placement Exam
   If your Python experience doesn’t come from one of the above options, you can demonstrate proficiency by taking our online Python placement exam. This exam is available after accepting a conditional admission offer.

Integral Calculus Requirement

To satisfy the Integral Calculus prerequisite, courses should cover integration, u-substitution and integration by parts. The following Seattle University courses meet these requirements:

• MATH 1335 – Calculus II: Theory, techniques and applications of integration; differentiation and integration of trigonometric, exponential and logarithmic functions; indeterminate forms; improper integrals.
• MATH 1230 – Calculus for Life Sciences: Limits; rate of change; derivatives, basic differentiation formulas, extrema; definite integral. Applications to life and social sciences.
• MATH 1130 – Elements of Calculus for Business: Limits; continuity; rate of change; derivatives; area under a curve; definite integral and applications.

Learn more about these courses in the Seattle University course catalog.

Students may also complete equivalent courses at a local community college or another regionally accredited institution, including online options. Be sure to reference the Transfer Equivalency Guide to explore your options.

Upon successful completion of the program, you'll be able to:

• Apply appropriate analytical and computational methods to solve real-world problems effectively.
• Communicate technical information effectively to a specific audience via speech, writing and data visualization.
• Exhibit constructive and inclusive collaboration and teamwork skills.