Course Description
Provides students with knowledge of the organizational context for data science, along with the architectural, methodological, governance, and ethical issues surrounding it.
Athena Title
Intro to Business Analytics
Prerequisite
(MSIT 3000 or MSIT 3000H or MSIT 3000E or BUSN 3000 or BUSN 3000E or BUSN 3000H) and (BUSN 4000 or BUSN 4000E)
Grading System
A - F (Traditional)
Course Objectives
The goal of this course is for students to understand organizational issues related to data science, to develop skills essential to analyze data, and to identify ethical issues related to data.
Topical Outline
• What is data science? • The evolution of data science • Various kinds of analytics (descriptive, predictive, prescriptive) • Linking the business and the analytics strategy • Identifying analytical opportunities • Making the business case for analytics (proposals, ROI) • Creating a data-driven decision-support culture • Creating the analytical architecture (sources, platforms, software) • Analytics governance • Organizing for analytics (staffing, organizational models, Centers of Excellence) • Developmental methodologies (agile, CRISP-DM, SEMMA) • Hands-on algorithm building exercise • Best practices case studies • Career opportunities in data science • Ethical issues (e.g., security, personal data, algorithmic transparency)
Syllabus