Post-Bacc Certificate in Data Science
Whether you're a recent graduate or a seasoned professional looking to transition into data science, our program is designed to accommodate learners at all stages. Recommended for those with an interest in mathematics, statistics, or computer science, the Post-Baccalaureate Certificate in Data Science provides foundational courses to kickstart your career in this dynamic field or pursue advanced studies. No prior educational background experience in data science is required.
The post-baccalaureate program is offered 100% online. You’ll develop skills in data analysis and problem-solving through hands-on projects. Prepare for a successful career in data science by joining our collaborative community of students and faculty.
Pathways
The ability to organize data, interpret its meaning, and communicate actionable insights is a valuable skill that everyone can bring to their work – and the post-baccalaureate program provides the ‘on-ramp’ that all professionals need to make impactful, data-driven decisions.
Choose between two distinct paths based on your professional goals:
- Certificate Track: Perfect for individuals seeking core expertise in data science without committing to the entire Master’s of Science in Data Science program. Build a strong foundation in data science with essential undergraduate courses and two graduate courses. Earn a prestigious certificate credential in a short time frame and advance your career.
- Master’s Track: Tailored to students and professionals who aspire to enroll in the Master of Science in Data Science program. Begin with foundational undergraduate courses in math and programming and advance to two graduate courses, ensuring transferable credits and seamless enrollment into the Master of Science in Data Science program.
Curriculum
The post-baccalaureate certificate and master's tracks consist of four fundamental undergraduate courses in mathematics and programming. These courses are followed by two graduate courses – probability theory and fundamentals of applied machine learning. One way of completing the curriculum is as follows:
| Semester | Credits | Course |
|---|---|---|
| 1 | 4 | CS 11 - Intro to Computer Science or CS 15 - Data Structures |
| 3 | CS/Math 61 - Discrete Math | |
| 3 | Math 32 - Calculus 1 (Differential Calculus)* | |
| 2 | 4 | CS 30 - Programming for Data Science |
| 3 | Math 70 - Linear Algebra | |
| 3 | Math 34 - Calculus II (Integral Calculus)* | |
| 3 | 3 | CS 135 - Introduction to Machine Learning^ |
| 3 | EE 104 - Probability Systems Analysis^ | |
| 3 | Math 42 - Calculus III (Multivariate Calculus)* |
*Students without a strong background in math may need to complete the calculus sequence.
^CS 135 and EE 104 will transfer into the MSDS program if the courses are completed with a grade of B- or better.