What is Data Science?
Data science is an interdisciplinary field centered on the scientific methods, processes, and systems used to extract knowledge or insights from data in various forms. Data can be structured (e.g. databases and spreadsheets) or unstructured (text, video, images).
Data science leverages tools from a variety of disciplines to:
- Gather data sets
- Process and derive insights from data sets
- Interpret insights for decision-making purposes
Data scientists have a mastery of computer science, statistics, mathematics, and a domain of application. Domain applications include business, medicine, and practically any area of science, engineering, or the humanities.
What degrees do you offer in data science?
The Goergen Institute offers Bachelor of Arts (BA), Bachelor of Science (BS), Master of Science (MS), and Advanced Certificate degrees in data science.
The data science BA and BS degrees are comprised of a group of pre-requisite courses, followed by upper level core courses in math, computer science, and data science. Students also complete a capstone project in their senior year and specialze in a concentration (application area), in addition to completing two upper level writing courses. Since data science is a natural science major, BA and BS degrees require clusters in the humanities and social science.
The Master of Science in data science is a full-time, two to three semester degree program that attracts students with undergraduate degrees in mathematics, statistics, computer science, natural sciences, social sciences, and engineering.
The advanced certificate degree is designed for individuals who have a working knowledge of data science gained through industry or academic experience but would like to formalize their training with a deeper mastery of the fundamental concepts in the field. The program can be completed in two to four semesters of part-time study.
What is an application area?
Data science can be applied to many different areas of natural science, social science, and business. A concentration (application area) is a set of three courses in one area of specialization. The Goergen Institute offers concentrations (application areas) in the following subjects:
- Biomedical signals and imaging
- Brain and cognitive sciences
- Computer science/statistics/mathematics
- Earth and environmental science
- Political science
We recommend that students consider which application area they would like to pursue during their first year of study. By doing so, they have more time to complete prerequisites to application area courses.
What is the difference between a BA and a BS in data science?
The Bachelor of Science (BS) in data science provides a more in-depth look into the core areas of data science, and is ideal for students interested in cutting-edge research and development careers in industry or academia. The BS degree requires additional mathematics courses that cover probability and statistics (MTH 201 and MTH 203), and an upper level course in computer science.
The Bachelor of Arts (BA) provides a more flexible course of study, and is good for students with interdisciplinary interests. It is popular with double majors and students interested in careers in a related discipline or industry.
Can I get a minor or cluster in data science?
At this time, we do not offer a minors or clusters in data science.
I’m a first year student. What should I take so I can declare data science as my major?
Incoming first year students typically take data science prerequisite courses to enter the major. This includes calculus (MTH 141/161/171) and MTH 150: Discrete Mathematics in the first semester, and CSC 171: The Science of Programming and a second calculus course (MTH 142/162/172) in the second semester.
During sophomore year, students take CSC 172: The Science of Data Structures and MTH 143 (if required). After completing the data science prerequisites, students must have a grade point averages of 2.0 or higher to pursue the degree.
In some cases, course requirements may be satisfied by AP credit or by testing, depending on the rules of the department that houses the course.
Incoming freshman should take a prerequisite towards their concentration (applications area) during their first year. The most common courses taken are PHY 121 (for biomedical signals and imaging), BCS 110 (for brain and cognitive sciences), and ECO 108 (for economics and business). Some application areas do not need prerequisites and can be started freshman year.
Will my Advanced Placement calculus and computer science be considered towards my curriculum in data science?
Calculus and computer science AP exam scores are evaluated by the Departments of Mathematics and Computer Science, respectively, to determine placement for MTH or CSC courses. Those departments also determine if a student is eligible to receive AP credit towards their degree.
Our goal is to make sure you are placed correctly so that you are neither over your head nor bored with class materials. We also want to ensure that students are adequately prepared for the next level course that they take.
Can I study abroad as a data science major?
Yes, absolutely! If you are interested in studying abroad, a four-year course plan can be worked out to see what semesters are optimum for your global experience.
Where do people with Data Science degrees go after graduation?
A majority of alumni from our department find jobs after graduation in industry working as data scientists and analysts and engineers for companies who deal with big data in technology, business consulting, healthcare, manufacturing and more. In addition, our students also attend graduate school for advanced degrees.
Do I need to have an internship during college?
While it is not required, we highly recommend doing one or more internships. An internship is a great way to gain on-the-job experience and can give you stronger options for job offers when you graduate.
The Greene Center frequently brings in employers and posts jobs and internships that are of interest for our students. We do not have a co-op program for students to work full time through the academic year.
What is the difference between data science and computer science?
Computer science is the development of hardware and software, the application of computer technology, and the study of computation theory.
Data science combines various aspects of computer science along with applied mathematics, statistics and application domain knowledge to develop automated methods to analyze massive amounts of data and extract knowledge from them.
A data scientist would rely on software tools and infrastructure developed and managed by computer scientists to access, store and process data to solve problems in a variety of application domains.
Can I double major in computer science and data science?
Computer science and data science share many courses, so double-majoring is not allowed. Students in data science can minor in CSC however. Computer science majors interested in data science and analytics, you may choose your advanced electives in CSC to specialize in courses that data science students are required to take (such as CSC 240, CSC 246, and CSC 261).
What is the difference between data science and statistics?
Data science emphasizes the data problems of the 21st Century, like accessing information from large databases, writing code to manipulate data, and visualizing data. Harvesting, processing, storing and cleaning are more central to data science.
See Priceconomic's What’s the Difference Between Data Science and Statistics? article for more information.
What is the difference between data science and business analytics?
Data scientists design, develop and deploy algorithms through statistical programming and supports business decision making tools. Business analysts research and extract valuable information from sources to explain historical, current and future business performances and determine analytical models to present and explain solutions to business users.
Data scientists often deal in more coding and programming to manage the large amounts of data and they create visualizations to aid in the understanding of the data. Data scientists frequently work in business but also can apply their knowledge to other fields.
For more information see this article comparing the two.