About the Program
With the rapid growth of data, companies, governments, and other institutions are striving to convert abundant untapped data into actionable knowledge and insights. Decision makers require evidence before resources are committed. Typically, commitments are not made unless evidence supports that the opportunities are both cost effective and yield positive net benefits. Data science is an interdisciplinary field that studies the processes, methods, techniques and systems to extract useful knowledge and insights from data in various forms that help drive evidence-based decisions. It has a wide range of applications in our society, from engineering to healthcare to business to social studies. It requires more programming, mathematics/statistics, modeling skills, and domain knowledge than what a traditional undergraduate curriculum offers.
One of the obstacles that must be removed before government, business and social sectors are prepared to use large datasets to enhance their decision-making, is the acquisition of a trained workforce that can leverage it. It has been suggested that the job market is facing a serious shortage of workers in data science. Data scientists lead the pack for best jobs in US, according to a recent report from company review site, Glassdoor.
Our BS in Data Science is a multidisplinary program that requires technical courses from all four colleges at the university. This multidisciplinary approach creates synergy among academic units at the university, provides flexibility in scheduling, and allows for timely completion of the program. Students with varied backgrounds can take different courses to suit their needs, based on interest and guided by faculty advisors. Through the program, they will build knowledge and skills that are essential in pursuing their data science careers.
Program Educational Objectives
Upon completion of the coursework, students in the data science core will have the ability to:
- Access, assess and manipulate data sets in order to extract meaningful information using statistical methods and mathematical models (Practical)
- Use the information gathered to build models capable of prediction and/or assessment (Practical)
- Communicate results and solutions in simple terms (Communications)
- Develop a clear understanding of ethical standards (Theoretical)