As our world changes and our everyday lives are transformed, our demand for and dependency on data are the driving forces that shape today’s Digital Economy. More and more industries hire diverse data science professionals who can weave data into actionable insights, algorithms, and business strategies. At Rutgers University – Newark, data science is recognized as a powerful mechanism for facilitating workforce development across disciplines.
Data science roles range from data analysts and data science modelers to machine learning engineers and artificial intelligence specialists. The demand for talent in data science continues to increase, and such demand provides opportunities to re-imagine how we prepare undergraduate students for data science careers. Rutgers University – Newark has been named the most diverse research university by U.S. News and World Report. Our undergraduate students who minor in data science comprise a new source of data science talent for the Digital Economy.
What can you expect from the Data Science Minor?
The Data Science Minor prepares diverse undergraduate students from all majors to become data science practitioners or highly skilled data science partners. Our interdisciplinary approach to data science has five guiding pillars:
- Exposure: From the moment you take your first course until you complete the 18 credits of the minor, you gain exposure to the field, research, and career opportunities that will broaden your understanding of data science and offer insights into the value of studying data science as you pursue your short-term and long-term goals.
- Experience: Our signature courses offer hands-on experience with in-demand data science tools and techniques.
- Ethics: Our approach to teaching and training you centers on ethics and the responsible transfer of knowledge. You learn about the legal and ethical implications of acquiring and analyzing data, and building models so that their applications advance the field and better our communities.
- Community: To set you up for success, you are immersed in a community of support that comprises the Data Science Learning Community, coaching and mentoring, and professional networking with experts in the industry, alumni in the field, professors across disciplines, and researchers at university labs and corporate departments.
- Career Readiness: The Data Science Minor has career readiness as its overarching goal. When you complete the minor, you will have the training, skills, and confidence to land competitive internships, engage in research opportunities, and start your first full-time opportunity in data science and data analyst positions.
Who should study Data Science?
As demand for data scientists and analysts increases beyond the corporate sector into government, education, and non profit organizations, professionals outside of the data science and computer science professions will require a working understanding of data science, data analysis, and the algorithms that drive their organization’s decision-making. Rutgers University – Newark, with its commitment as an anchor institution, and to diversity and inclusion, holds a unique position in ensuring that all students have access to the education and experts required to develop future professionals. Therefore, undergraduate students from all majors who seek to benefit from an education in the collection, analyzation, and summarization of data using computation and data science tools and techniques are encouraged to declare, pursue, and complete the Data Science Minor.
The Data Science Minor is open to all undergraduate students of Rutgers University – Newark.
A total of 18 credits are required for the Data Science Minor.
Students take four required courses for a total of 12 credits:
21:219:105 Everyday Data (3 credits)
21:219:220 Fundamentals of Data Visualization (3 credits)
21:219:330 Ethical Issues in Data Science (3 credits)
21:198:431 Database System Design and Management (3 credits)
Students take a minimum of 6 credits from the following electives:
21:219:329 Statistics and Machine Learning (3 credits)
21:220:350 Computing for Economics (3 credits)
21:219:400 Deconstructing Machine Learning Bias (3 credits)
21:219:420 Agile iOS Design and Development (3 credits)