Data Science and Analytics
DSA 501 DATA ORIENTED COMPUTING AND ANALYTICS
Prerequisite: Instructor permission. Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports. Offered every spring.
DSA 512 INTRODUCTION TO DATA SCIENCE AND ANALYTICS
Prerequisites: Graduate standing. Introduction to Data Science and Analytics; modern analytical techniques; application to academia, industry and business needs. Fundamental concepts and terms; methods, tools, and techniques; identification of “big data” problems; data sources; analytical approaches; algorithm implementations; interpretation and reporting of results. Offered annually in the Fall semester.
DSA 600 MACHINE LEARNING FOR DATA SCIENCE
Prerequisites: CIS 512 or DSA 512 or equivalent. Introduction to Machine Learning Techniques for Data Science; mathematical methods; algorithms; application to academia, industry and business problems. Fundamental concepts and terms; methods, tools, and techniques. Supervised and unsupervised learning; identification of learning problems; data sources; analytical approaches; algorithm implementation; interpretation and reporting. Offered annually in the Fall semester.
DSA 616 ELEMENTS OF MATHEMATICS, PROGRAMMING AND COMPUTER SCIENCE FOR DATA SCIENCE
Prerequisite: Instructor permission. Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods. Offered every fall semester.
DSA 646 INTRODUCTION TO STATISTICS FOR DATA SCIENCE
Prerequisite: Instructor permission. Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance. Offered every fall semester.
Equivalent Course: MAT 646
DSA 650 DATA STRATEGY AND GOVERNANCE
Prerequisite: Instructor permission. Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security. Offered every fall semester.
DSA 690 MASTER'S PROJECT