As data accumulates across broad sectors of industry and academia we see a need for data scientists equipped with skills to assist with data-based decision making. For example, businesses are using data to determine insurance coverage, to make marketing decisions, to offer recommendations to customers, and to provide more effective health care. A famous example from academia is the determination of the Higgs Boson from simulated data with machine learning methods.
We offer a Masters in Data Science degree that covers basic and advanced essentials in statistical inference, machine learning, data visualization, data mining, and big data methods, all of which are key for a trained data scientist. In order to be selected for our program, we require a basic background in calculus, linear algebra, probability, computer programming, data structures, and algorithms. Our program is spread across 30 credits and contains projects involving big datasets, classification methods, variable selection, and deep learning to name a few.
In our curriculum, we make extensive use of the Python programming language and its data science libraries while also featuring tools like R for statistical analysis, Tableau for data visualization, and SQL for databases. Students work on assignments covering both theory and applications on real data with support available from the professor and teaching assistant.
Our career services office assists students with resume preparation and reaching out to companies in need of data scientists. While business publications like the Harvard Business Review have written about the lucrative prospects of data science, a search on the career website indeed.com for "data science" reveals a considerable number of opportunities in the New Jersey and New York region.
As described in the curriculum linked below, the program contains two tracks: a Computational Track, and a Statistics Track.
Students in the Master in Data Science (MSDS) program must successfully complete 30 credits based on any of the following options:
- Courses (30 credits)
- Courses (27 credits) + MS Project (3 credits)
- Courses (24 credits) + MS Thesis (6 credits)
Independent of the chosen option, all core courses in the respective tracks are required.
At most two courses can be chosen from outside the respective track with the approval of the respective Program Co-Directors. Computational track students are allowed at most three electives that are nonComputer Science courses. Statistics track students are allowed at most three electives that are non-Math courses.
If a student chooses the MS project or MS thesis option, the project or thesis must be related to data science and requires approval from one of the Program Co-Directors.
The MSDS program has computational and statistics tracks that students must choose from at admission time. These tracks have different core courses but share the same admission requirements and electives.
Students may choose an elective outside the list after approval of their respective advisor.