Data Driven Decision Making: Learn Quantitative Analysis and

GSBA 545-Data Driven Decision Making Syllabus-Fall 2022 Professor:Inga Maslova Office:ACC 203 Class:M 12:30 pm-1:50 pm in JKP 104 Office Hours:Monday 9:00 am - 10:00 am PSTon Zoom or by appointment (Click here to schedule) E-mail:[email protected] COURSE DESCRIPTION Data Driven Decision Making will teach students to become more savvy business professionals through quantitative analysis. After this course, students will be able to think quantitatively and properly interpret data-oriented statements. We will cover fundamental statistical techniques in a managerial setting, with examples and concrete exercises from business and non-business settings. Statistical topics include effective use of numerical and graphical summaries, important probability distributions, estimation and confidence intervals, hypothesis testing, categorical data analysis, and multiple linear regression analysis. The important 'big picture' goal of this course is to think aboutthe process of decision making under uncertainty, a necessary skill in all business professions. Over the last two decades, we have witnessed an explosion in the availability of data.Firms routinely collect point of sales transactions, monitor operating performance throughout their supply-chain, mine website traffic, and track customer engagement.Business analytics and data are transforming modern firms, and, in some cases, disrupting entire industries. Importantly, these changes are not limited to the "back-office" or operations; every aspect of the firm- organizational structure, marketing, product design, and strategic planning-is shifting towards data-driven decision-making.With this shift comes an increased need for "data-savvy"analysts; analysts who are not necessarily data-science experts, but understand what analytics can and cannot do, how to ask the right questions, and, most importantly, how to interpret data to make better decisions. LEARNING OBJECTIVES At the end of this course, you will be able to: •Explain in your own words the key ideas behind fundamental techniques in data analytics •Identify new opportunities to use these techniques across business domains to guide decision-making •Confidently apply these techniques to practical problems using R •Formulate and communicate actionable business recommendations based upon your analysis, including its limitations •Critically assess the validity of analytics-based recommendations in the context of specific business decisions COURSE BOOK •An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, and Tibshirani. The book's website ishttp://www-bcf.usc.edu/~gareth/ISL/index.htmlAlso, USC has subscription to Springer, so you should be able to access the book online: http://link.springer.com/book/10.1007/978-1- 4614-7138-7/page/1 •Additional resource (optional): I would also recommend that you obtain a copy of "An Introduction to R" by Venables and Smith which we will use as a manual for learning R. You can download it for free from here