Degree Program in Data Science and Business Analytics

The Online Degree: Data Science and Business Analytics Program is designed for learners who want a structured way to study how data is collected, cleaned, analyzed and turned into decisions. It brings together statistics, computing and business reasoning so that numbers can answer practical questions about organizations and the communities they serve. The program suits learners from varied backgrounds, including those working in public agencies, nonprofits and local government who need to read data with confidence. Its intended outcomes are a working understanding of analytical methods, clear reasoning about evidence, and the ability to communicate findings to people who make decisions.

The curriculum is organized into a set of connected subject areas. The first area covers the foundations of quantitative thinking, including probability, descriptive and inferential statistics, and the basics of data structures. A second area introduces programming and data handling, covering how datasets are stored, queried, transformed and checked for quality. These areas provide the vocabulary and tools used throughout the rest of the program.

Later subject areas focus on analytical modeling, business measurement and data communication. Modules in these areas address regression and forecasting, performance metrics, decision analysis and visualization. Each module is paired with case material so that methods are tied to realistic questions. Knowledge is spread across the areas so that technical skill and business interpretation develop together rather than in isolation.

Learning progresses from foundational to advanced concepts. A learner first becomes comfortable with summarizing a dataset and checking its reliability before moving on to models that predict outcomes or compare alternatives. Modules are sequenced so that each one relies on skills introduced earlier, which keeps the material cumulative. For example, a learner who has studied sampling and error can later evaluate whether a trend in service-use records reflects real change or simply noise.

The subject areas are brought together through applied case work. In a public service setting, a learner might examine attendance records at a community center to decide where outreach resources would reach the most residents, or review response times for emergency services to identify patterns that warrant closer study. Each case draws on statistics for measurement, programming for data preparation and business reasoning for weighing costs and benefits, so the learner practices integrating all three in one analysis.

Completion of the program is marked by a final set of integrated analyses in which the learner defines a question, prepares the relevant data, applies appropriate methods and presents conclusions with stated limits. This stage consolidates earlier learning and shows how the subject areas work together on a single problem. Learners may also review and refine their analytical portfolio as a record of their applied work.

Further progression can take several directions. Some learners move into more specialized study in areas such as machine learning, economic evaluation or program evaluation, while others deepen their work in data governance and ethical use of information. Continued academic or professional development can build on the program's foundations through advanced coursework, independent projects and ongoing practice with new datasets and analytical tools.