BS DATA SCIENCE

If you’re an educational professional who are looking to progress into management and consultancy, or an educational planning or development role, this is the best degree for you.

BS (Data Science) has a dual emphasis on basic principles of statistics and computer science, with foundational training in statistical and mathematical aspects of data analysis. This program, in addition, develops foundation on broad computer science principles, including algorithms, data structures, data management and machine learning. The program is suitable for students interested in either a career in industry or who wish to pursue more specialized graduate study. This program  will prepare students for a career in data analysis, combining foundational statistical concepts with computational principles from computer science. A major component of this degree is the final year two-semester project, that teaches students how to apply statistical and computational principles to solve large-scale, real-world data analysis problems. BS (Data Science) is a four year degree program. It requires completion of 144 credit hours of course work and 2 credit hours of internship of at least six weeks at an organization approved by the Institute.

Data Science students learn to:

• Define information needs of individuals and organizations; 
• Select and transform data to increase usefulness for solving particular problems; 
• Analyze and synthesize unstructured data to create actionable information; 
• Create information visualizations for data exploration and presentation; 
• Manage very large volume data sources from acquisition through disposal; 
• Secure and preserve data in ways consistent with legal and organizational considerations

Learning Outcomes for Data Science Certificate students include:

1. Knowledge of how to apply analytic techniques and algorithms (including statistical and data mining approaches) to large data sets to extract meaningful insights.
2. Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing and visualization.
3. Ability to communicate results of analysis effectively (visually and verbally) to a broad audience.