Data Science Major

Data has become ubiquitous in everyday life, impacting every profession, from entry-level office workers to CEOs, from team coaches to general managers, from accountants to CFOs. Businesses now have data available to them at a scale that is historically unprecedented; harnessing this data for insight on what customers want provides them with a competitive advantage. Traditional companies (Ford, Walmart, General Electric, etc.) today pride themselves as being transformed to big-data businesses. Furthermore, data permeates all aspects of science, engineering, and other academic disciplines.

The field of Data Science has recently emerged as a new academic discipline studying data itself. Data Science lies at the intersection of Computer Science and Statistics. By creating a Data Science undergraduate degree we aim to educate the future data scientists and leaders in this field, by building knowledge bottom-up, covering both essential knowledge from Computer Science (in managing data) and Statistics (in analyzing data), and integrating this knowledge with applications to other domains and to real-life problems. The Data Science major provides a comprehensive program studying how data can be collected, transformed, analyzed, and used to solve problems across academic disciplines and applications.

Curriculum

Through its interdisciplinary nature, the Data Science major (B.S.) offers a great opportunity to serve as a pathway for professional careers in various areas. A distinguishing characteristic of our Data Science program in that its students will complete course sequences in other departments (e.g., economics, business, sociology, earth sciences, biology, bioinformatics, and astronomy) where they will learn how Data Science principles are applied in these domains.

Fueled by the explosion of data, Data Science jobs have proliferated and the demand for data scientists is extremely high; moreover, this demand is expected to be strong for years to come. A recent McKinsey report forecasted a need for hundreds of thousands of data scientists in the next decade. Three-fifths of the data science and analytics jobs are in the finance and insurance, professional services, and information technology sectors, but the manufacturing, health care, and retail sectors also are hiring significant numbers of data scientists. According to Glassdoor, a recruiting site, Data Scientist has been among the top jobs in the US for various years, attracting high salaries. We thus expect that the Data Science program will provide new opportunities and serve well the UCR community.

The B.S. in Data Science is an intercollegiate STEM major offered by the Department of Computer Science and Engineering (within the Bourns College of Engineering) and by the Department of Statistics (within the College of Natural and Agricultural Sciences). When students declare the major, they choose from which college they wish to have their degree awarded. Students whose degrees are awarded by the Bourns College of Engineering are advised in and have their records maintained by the BCOE Office of Students Academic Affairs; students whose degrees are awarded by the College of Natural and Agricultural Sciences are advised in and have their records maintained by the CNAS Undergraduate Academic Advising Center. Breadth requirements vary by college; and students must fulfill the breadth requirements of the college they choose.

Courses

The Data Science major offers a high quality program. Its design was greatly inspired by two recent reports about creating undergraduate Data Science programs from the National Academies of Sciences, Engineering and Medicine and the Park City Mathematics Institute (an NSF report endorsed by the Board of Directors of the American Statistical Association).

More information on the Data Science Program Catalog Description.

Students choosing their BS in Data Science from BCOE should fulfill the requirements laid out in the BCOE Data Science course plan.
Students choosing their BS in Data Science from CNAS should fulfill the requirements laid out in the CNAS Data Science course plan.

To help students with planning their coursework, below are the undergraduate course offerings by the two departments:
Computer Science and Engineering: https://www1.cs.ucr.edu//undergraduate/course-listings                                   
Statistics:  2023-24 Statistics Undergraduate Course Listings

Program Educational Objectives 

Graduates of UCR’s BS degree program in Data Science will meet high professional, ethical, and societal goals as demonstrated by:

• Success in post-graduation studies as evidenced by:
  • satisfaction with the decision to further their education
  • advanced degrees earned
  • professional visibility (e.g., publications, presentations, patents, inventions, awards)
• Professional responsibilities (e.g. professional mentoring, professional society membership and offices, reviewing and editorial work for professional journals) success in a chosen profession or vocation as evidenced by:
  • career satisfaction
  • promotions/raises (e.g. Management leadership positions or distinguished technical positions)
  • professional visibility (e.g., publications, presentations, patents, inventions, awards)
  • professional responsibilities (e.g. professional registration, professional mentoring, professional society membership and offices)
  • entrepreneurial activities
  • consulting activities
• Contributions to society as evidenced by:
  • leadership roles
  • public service
  • mentoring / outreach activities
  • volunteer service

Student Outcomes

Our goal for our graduates
As a graduate of our program, you will be able to move onto the next chapter of your life with the following skills:

1) Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
2) Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
3) Communicate effectively in a variety of professional contexts.
4) Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
5) Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
6) Apply theory, techniques, and tools throughout the data analysis lifecycle and employ the resulting knowledge to satisfy stakeholders’ needs.
7) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.