This course describes the premise of the capstone—what it entails, its purpose, and an outline of work required to fulfill the capstone project requirements. Students are provided with an overview of the capstone course objectives—how to prepare and organize for a semester-long project, the methods used to develop a project, descriptions of project options, and
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will
Explore procedures and techniques for using data to inform the decision-making process. Topics include optimization, decision analysis, game theory, and simulation. Prerequisites: DS 705, DS 710 DS 775 Syllabus
Investigate the ethical issues in computer science that ultimately also pertain to data science, including privacy, plagiarism, intellectual property rights, piracy, security, confidentiality, and many other issues. Your study of these issues will begin broadly, with a look at ethical issues in computer science at large. We will then make inferences to the narrower field
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 DS 745 Syllabus
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 700, DS 710 DS 740 Syllabus
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 DS 730 Syllabus
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. DS 715 Syllabus
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working
