The graduate faculty in the Department of Statistics and Biostatistics offers
a master of science concentration in data science. This program is designed to
meet the emerging demand for data science and data analytics professionals.
With advanced data
collection capability and advanced computation capability, analyzing big data
and extracting useful information from massive data has become one of the
emerging frontiers of statistics and related fields. As modern information
technologies relentlessly generate voluminous and complex data, analytical
tools with a solid foundation in statistical theory and methodology have become
indispensable to industry and society in general.
Due to the
interdisciplinary nature of the new generation of analytics requirements, a
successful and vital data science program requires a special approach toward
the training of the next generation of experts and leaders. Data science has
deep roots in probability and statistical inference. This program is designed
to provide its graduates with (i) a deep understanding of the nature of
uncertainty and statistical inference principles; (ii) strong technical skills
for data management and data analysis; and (iii) the necessary communication
and leadership skills for a successful career as a data science professional.
The program
involves highly motivated faculty members and industry experts in a truly
interdisciplinary approach. It will prepare graduates for immediate employment.
The program is designed for individuals with an undergraduate
degree in science or engineering. Students with a more quantitative background
are preferred. Strong candidates with a partial deficiency in background
knowledge will be allowed to take additional prerequisite courses recommended
by the program adviser. The program welcomes part-time students with strong
mathematical and statistical backgrounds. In order to accommodate the demanding
schedule and workload of part-time students, most of the courses will be
offered in the evening. The courses will be open to students in other related
programs at Rutgers.
The students in the new program will need to
complete 30 course credits to graduate. Among the 10 courses, eight are
required courses and two are electives; students must also submit an
approved master's degree essay. The required courses are: 16:954:581
Probability and Statistical Theory for Data Science
16:954:596 Regression and Time Series Analysis for Data Science
16:954:597
Data Wrangling and Husbandry
16:198:512
Data Structures and Algorithms
16:198:539
Database Systems Implementation
16:954:567 Statistical Models and Computing
16:954:534
Statistical Learning for Data Science
16:958:588 Financial Data Mining
The two elective courses may be chosen from a list of
courses offered by graduate programs in statistics, computer science, business, and
the graduate programs in economics, electrical and computer engineering, and mathematics.
There is no comprehensive examination.
Students are encouraged to seek summer internships in the
financial industry. A report on a suitable summer internship project can serve
as the basis for the master's degree essay.
Students may pursue the degree full time or part time.
Full-time students can complete the degree in three semesters and part-time
students must complete their degree within five years. The minimum cumulative
grade-point average required for graduation is 3.0 (B) for all courses taken at
Rutgers after admission to the program.
It also is possible to apply for admission to the master of science concentration in data science as a
nondegree student. As many as 12 credits of coursework taken as a nondegree
student can count toward the degree if the student is subsequently admitted to
the degree program.
Programs, Faculty, and Courses 
