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Actuarial and Statistical Analysis
African Studies 016
Analytics: Discovery Informatics and Data Sciences
Anthropology 070
Applied Computing
Art History 082
Arts, Visual and Theater
Asian Studies 098
Atmospheric Science 107
Biochemistry 115
Bioenvironmental Engineering 116
Biomedical Engineering 125
Biotechnology 126
Biotechnology and Genomics
Business and Science 137
Cell and Developmental Biology 148
Cellular and Molecular Pharmacology
Chemical and Biochemical Engineering 155
Chemistry
Chemistry and Chemical Biology 160
Chinese 165
Cinema Studies 175
Civil and Environmental Engineering 180
Classics 190
Cognitive Science 185
College Teaching 186
College and University Leadership 187
Communication, Information and Library Studies 194
Communication Studies
Comparative Literature 195
Computational and Data-Enabled Science and Engineering 199
Computer Science 198
Cultural Heritage and Preservation Studies (CHAPS)
Curatorial Studies
Data Science (Statistics Track) 954
Program
Graduate Courses
Drug Discovery and Development
East Asian Languages and Cultures 217
Ecology and Evolution 215
Economics 220
Education 300
Educational Psychology; Educational Theory, Policy, and Administration; Learning and Teaching
Electrical and Computer Engineering 332
Endocrinology and Animal Biosciences 340
Energy 335
Engineering Management
English, Literatures in (English 350, Composition Studies 352)
English as a Second Language 356, American Language Studies 357
Entomology 370
Environmental Change, Human Dimensions of 378
Environmental Sciences 375
Exposure Science
Financial Statistics and Risk Management 958
Food and Business Economics 395
Food Science 400
French 420
Genetic Counseling
Geography 450
Geological Sciences 460
Geospatial Information Science 455
Geospatial Information Systems
German 470
Global Agriculture
Global Sports Business 475
Graduate Student Professional Development 486
Higher Education 507
Historic Preservation
History 510
Horticulture and Turfgrass Science
Human Resource Management
Industrial Mathematics
Industrial Relations and Human Resources 545
Industrial and Systems Engineering 540
Information Technology
Interdisciplinary Ph.D. Program 554
Italian 560
Jewish Studies 563
Kinesiology and Applied Physiology 572
Labor and Employment Relations
Landscape Architecture 550
Latin American Studies
Library Studies
Linguistics 615
Literature and Language 617
Literatures in English
Management
Materials Science and Engineering 635
Mathematical Finance 643
Mathematics 640, 642, 644
Mechanical and Aerospace Engineering 650
Medical Device Design and Development
Medicinal Chemistry 663
Medieval Studies 667
Meteorology
Microbial Biology 682
Microbiology and Molecular Genetics 681
Molecular Biophysics 696
Molecular Biosciences 695
Music
Music 700
Neuroscience 710
Nutritional Sciences 709
Oceanography 712
Packaging Engineering 731
Perceptual Science 714
Personal Care Science
Pharmaceutical Engineering
Pharmaceutical Science 720
Pharmaceuticals and Clinical Trials Management 725
Pharmacology, Cellular and Molecular 718
Pharmacy
Philosophy 730
Physics and Astronomy 750
Physiology and Integrative Biology 761
Planning and Public Policy 762
Plant Biology 765
Political Science 790
Psychology 830
Psychology, Applied and Professional
Public Health 832
Public Policy
Quality and Reliability Engineering
Quantitative Biomedicine 848
Quaternary Studies 855
Religion 840
Russian, Central and East European Studies 859
Science and Technology Management 885
Social Networking and Media
Social Work 910
Social Work: Administration, Policy and Planning, and Direct Practice
Sociology 920
Spanish 940
Statistics and Biostatistics 960
Sustainability
Theater Arts
Toxicology 963
United Nations and Global Policy Studies
Urban Environmental Analysis and Management
Urban Planning, City and Regional
User Experience Design (UXD)
Visual Arts
Women's and Gender Studies 988
Writing for Graduate Students 355
Research Centers and Institutes
Administration
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Graduate Courses

Camden Newark New Brunswick/Piscataway
Catalogs
  Graduate School-New Brunswick 2017 Programs, Faculty, and Courses Data Science (Statistics Track) 954 Graduate Courses  
Graduate School-New Brunswick
16:954:534 Statistical Learning for Data Science (3) Advanced statistical learning methods essential for applications in data science. Course covers optimization, supervised and unsupervised learning, trees and random forest, deep learning, graphical models, and others.
16:954:567 Statistical Models and Computing (3) Advanced statistical models and computing methods essential for applications in data science. Course covers inference of multivariate normal distribution and multivariate regression, nonparametric regression, bootstrap and EM, Bayesian analysis, and MCMC method.
16:954:577 Advanced Analytics Using Statistical Software (3) Modeling and analysis of data, usually very large datasets, for decision making. Review and comparison of software packages used for analytics modeling. Multiple and logistic regression, multistage models, decision trees, network models, and clustering algorithms. Investigate data sets, identify and fit appropriate data analytics models, interpret statistical models in context, distinguish between data analytics problems involving forecasting and classification, and assess analytics models for usefulness, predictive value, and financial gain.
16:954:581 Probability and Statistical Theory for Data Science (3) The study of probabilistic and inferential tools important for applications in data science. Topics covered: probability distributions; decision theory, Bayesian inference, classification, prediction; law of large numbers, central limit theorem; point and interval estimation; multiple testing, false-discovery rates.
16:954:596 Regression and Time Series Analysis for Data Science (3) This course introduces regression methods, state space modeling, linear time series models, and volatility models, which are important tools for data analysis, and are foundations for developing more specialized methods.
16:954:597 Data Wrangling and Husbandry (3) This course provides an introduction to the principles and tools to retrieve, tidy, clean, and visualize data in preparation for statistical analysis. Principles of reproducibility and reusability are emphasized. It teaches techniques to wrangle and explore data. The emphasis is on preparation of data to ease the analysis rather than sophisticated analyses. Topics include methods to convert data from diverse sources into suitable form for data visualization and analysis; methods to scrape data from websites; data visualization; elementary database operations such as SQL's join; construction of web-based analysis apps; and principles of reproducibility and reuseability, including literate programming, unit tests, and source code management.
16:958:588 Financial Data Mining (3) Databases and data warehousing, exploratory data analysis and visualization, an overview of data mining algorithms, modeling for data mining, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, observational studies. Emphasis on the use of data mining techniques in finance and risk management. Prerequisites: 16:958:563, and 16:198:443 or equivalent C++ course or permission of instructor.
16:958:589 Advanced Programming for Financial Statistics and Risk Management (3) This course covers the basic concepts of object-oriented programming and the syntax of the Python language. The course objectives include learning how to go from the different stages of designing a program (algorithm) to its actual implementation. This class lays the foundation for applying Python for interactive financial analytics and financial application building.
16:198:512 Introduction to Data Structures and Algorithms (3) An introduction for students in other degree programs on data structure and algorithms.
16:198:521 Linear Programming (3) Linear inequalities, extreme points and rays, fundamental theorems. Optimality and duality. Geometric view. Primal and dual simplex methods. Degeneracy. Primal-dual method. Sensitivity. Basis factorization, implementation issues. Column generation. Structured models. Network simplex method and unimodularity. Polynomial-time algorithms for linear programming. Kalantari. Prerequisites: Linear algebra and admission requirements.
16:198:539 Theory of Computation (3) Mathematical theory of computing machines. Computable functions, recursive and recursively enumerable sets, recursion and fixed-point theorems, abstract complexity and complexity theoretic analogues of aspects of recursive-function theory, algorithmic (Kolmogoroff) complexity theory. Allender. Prerequisite: 16:198:509 or equivalent.
16:198:541 Database Systems (3) Relational data model. Relational query languages and their expressiveness. Dependency theory and relational normalization. Physical database design. Deductive databases and object-oriented databases. Optimization of relational queries. Borgida. Prerequisites: 01:198:336 or equivalent; 16:198:513. Recommended: 16:198:509 or equivalent.
16:332:509 (S) Convex Optimization for Engineering Applications (3) Theory, algorithms, and tools to formulate and solve convex optimization problems that seek to minimize cost function subject to constraints; engineering applications. Dana
16:332:562 (S) Visualization and Advanced Computer Graphics (3) Advanced visualization techniques, including volume representation, volume rendering, ray tracing, composition, surface representation, and advanced data structures. User interface design, parallel and object-oriented graphic techniques, and advanced modeling techniques. Prerequisite: 16:332:560.
16:960:688 Bayesian Data Analysis (3) Bayesian inference, manipulation of joint probability distributions, probability distributions, and conditional independence concepts; Monte Carlo methods, static and dynamic methods; predictive approach to Bayesian analysis, exchangeability, and the de Finetti theorem; Bayesian analysis in one-layer problems, including prior, posterior, and predictive distributions; and Monte Carlo methods in advanced modeling and inference problems. Calculations are done in the R computer language and BUGS, a software package for Bayesian data analysis. Prerequisite: 16:960:593.
 
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