<aside> 🆕 This documentation shows an updated version of this program that began its roll-out during the 2024-2025 academic year. Courses from the prior version will run until they are no longer needed to ensure graduation timelines.
The previous documentation can be found here:
A summary of the changes made in this update can be found here:
Data Analytics Program Refresh Summary
</aside>
There is an unfathomable amount of data in the world, and new data is generated every second of every day. In virtually every sector, ranging from business and finance to healthcare, the demand for Data Scientists is only growing. Data Scientists harness data to inform decision-making, improve operational efficiency, and foster innovation.
This program targets jobs like Data Scientist, Technical Data Analyst, and Machine Learning Engineer. The Bureau of Labor Statistics reports that Data Scientist jobs offer a median salary of $103,500, and are growing more than 3x faster than average, so students who complete this program are prepared for well-compensated, in-demand careers.
Our Data Science program begins with two foundational Data Analytics Core courses, introducing students to data manipulation and data visualization using spreadsheets. Students advance to using Python and cutting-edge machine learning to create predictive models and work with unstructured data. This program emphasizes predictive modeling and AI systems, such as Large Language Models (LLMs), and is technically rigorous.
This program is deliberately smaller than many other programs in Data Science to allow students to pursue their interests in the liberal arts, and consider how their newfound understanding of data might shape how they look at problems in other fields. This is done in a reduced course load by focusing on high-demand technical specializations in upper-level courses: Machine Learning and Large Language Models. Its design has benefited from over three years of continuous refinement, incorporating valuable feedback from instructors, students, and curriculum committees, and extensive testing of a prior version of this program.
Our curriculum committees are staffed by subject matter experts from industry and academia, and both populations agree that a two-track program will improve student outcomes and experience. While students in the existing courses have successfully achieved the technical learning outcomes, the time dedicated to learning coding skills has resulted in delays in students performing meaningful analysis in the introductory courses.
| Member | Organization | Notes |
|---|---|---|
| Ehren Bucholtz | University of Health Sciences and Pharmacy | Academic representative |
| Kasey Chermak | Keystone College | Academic representative |
| Fred Sakon | Nunez Community College | Academic representative |
| Olawale Olasupo | Florida Institute of Technology | Academic representative |
| Brandon Bean | Utica University | Academic representative |
| Matthew Turk | Cleanlab | Industry representative |
| Charles Elkan | ficc.ai | Industry representative |
| Ben Lynton | 10ahead.ai | Industry representative |
| Kris Jamsa | Praxis Precision Medicines, Inc. | Industry representative |
If you have any questions about technology requirements for Rize courses, please see below:
Technology requirements for Rize courses - AY 2025-2026
Recommended CIP Code: 30.7001 or 30.7101
Note: Courses provided through the LCMC are shown in blue text