Big Data Essentials
The Big Data Essentials certificate introduces students to the core concepts and technologies used to handle large-scale, complex data environments. Rather than focusing solely on analysis, the program emphasizes the full data pipeline, including data storage, processing frameworks, and scalable system design. Students explore how modern data ecosystems operate and develop the technical ability to work with distributed systems and high-volume data platforms. This certificate is suited for individuals seeking to build practical expertise in managing and leveraging data in today’s data-driven industries, while also laying the groundwork for advanced study.
Prerequisites
Applicants should have a bachelor’s degree from an accredited institution in a STEM discipline or have relevant professional experience in computing. Further information can be found here.
Related MS Programs
Students who achieve a GPA of at least 3.0 are assured of admission into MS programs offered by the Ying Wu College of Computing. Courses within this certificate program may fulfill the degree requirements for the MS in DS, MS in AI, or MS in BNFO program. Current students may also reach out to YWCC advisors for additional information.
Degree Requirements
The graduate certificate in Big Data can be completed by taking four courses (12 credits). The requirements must be satisfied as indicated in the following Course List.
| Code | Title | Credits |
|---|---|---|
| Core Courses | 6 | |
| DS 644 | Introduction to Big Data | 3 |
| or CS 644 | Introduction to Big Data | |
| DS 637 | Python and Mathematics for Machine Learning * | 3 |
| Electives | 6 | |
| Select two of the following: | ||
| CS 632 | Advanced Database System Design | 3 |
| CS 643 | Cloud Computing | 3 |
| CS 670 | Artificial Intelligence | 3 |
| DS 642 | Applications of Parallel Computing | 3 |
| DS 675 | Machine Learning | 3 |
| or CS 675 | Machine Learning | |
| DS 732 | Theoretical Foundation of Machine Learning | 3 |
| or CS 732 | Advanced Machine Learning | |
| IS 601 | Python for Web API Development | 3 |
| IS 665 | Data Analytics for Info System | 3 |
| MATH 661 | Applied Statistics | 3 |
| Total Credits | 12 | |
* DS 637 is recommended as an introductory course, offering a review of mathematics for machine learning to students with a limited background in mathematics or programming.
