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Actuarial and Statistical Analysis
African Studies 016
Analytics: Discovery Informatics and Data Sciences
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Biochemistry 115
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Cultural Heritage and Preservation Studies (CHAPS)
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Data Science (Statistics Track) 954
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Drug Discovery and Development
East Asian Languages and Cultures 217
Ecology and Evolution 215
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Education 300
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Electrical and Computer Engineering 332
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Entomology 370
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Exposure Science
Financial Statistics and Risk Management 958
Food and Business Economics 395
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French 420
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Industrial and Systems Engineering 540
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Italian 560
Jewish Studies 563
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Mathematics 640, 642, 644
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Medieval Studies 667
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Microbiology and Molecular Genetics 681
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Molecular Biosciences 695
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Packaging Engineering 731
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Camden Newark New Brunswick/Piscataway
Catalogs
  Graduate School-New Brunswick 2017 Programs, Faculty, and Courses Data Science (Statistics Track) 954 Program  
Graduate School-New Brunswick

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.

 
For additional information, contact RU-info at 732-932-info (4636) or colonelhenry.rutgers.edu.
Comments and corrections to: Campus Information Services.

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