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UW-Madison GEN BUS 306 - Syllabus

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Lectures:Course DescriptionLearning ObjectivesFriday DiscussionsCreditsCanvasSoftwareTextbookExamsCasesHomeworkGrading & Key DatesBook Chapters & Reading AssignmentsLaptop PolicyOtherAdditional ResourcesGB 306: Business Analytics I – Spring 2018 Page 1 of 7 Copyright © 2018 by Hessam Bavafa, Richard Crabb, and Anita Mukherjee Course Syllabus // Business Analytics I (GB 306) Instructor: Richard Crabb Office hours: Thursday 12:00 – 2:00 PM 5192 Grainger [email protected] Teaching Assistants (and their office hours) Vaibhav Anand “Vab-huv” Xiao Ming Gu “Ming” Ryan Thielen Kenny Wunder Tu 11-12p 2167 Grainger M 3-4p 2167 Grainger Th 8:15-9:15a 2167 Grainger Tu 8:15-9:15a 2167 Grainger vaibhav.anand [email protected] [email protected] kenny.wunder All TA office hour locations are in 2167 and will begin the week of January 29. Lectures: All lectures on a given day will cover the same material. If you cannot make your assigned lecture, please feel free to attend a different lecture. The same is true for discussion. Lectures are on Monday and Wednesday: • 9:55 – 10:45am in Grainger 2080 • 11:00 – 11:50am in Grainger 2080 • 4:30 – 5:20pm in Grainger 2120 Discussions are on Friday: • 8:50 – 9:40am in Grainger 2170 • 9:55 – 10:45am in Grainger 1270, 2170 • 11:00 – 11:50am in Grainger 1270, 2170 • 12:05 – 12:55pm in Grainger 1270, 2170 Course Description How can insurance companies detect fraudulent claims? Are there racial or gender biases in the university admissions process? What factors should a sports team prioritize when deciding whether to draft, release, or trade a player? What is the fastest way for planes to boardGB 306: Business Analytics I – Spring 2018 Page 2 of 7 Copyright © 2018 by Hessam Bavafa, Richard Crabb, and Anita Mukherjee passengers? Answering these questions requires a knowledge of analytical methods and familiarity with quantitative reasoning. Competency in analytics – the ability to ask the right questions, make sense of structured and unstructured data, translate analytic insights into actions that influence business decisions – is necessary for modern organizations to succeed. In this course, you will develop your quantitative intuition through practical application using Excel. Specifically, you will learn how to produce summary statistics in both tabular and visual forms using data. You will also learn the essentials of probability and apply it to decision problems where there is uncertainty. As we develop our learning, we will emphasize hypothesis testing and regression analysis, with an introduction to simulation methods. Throughout this course, we will pay special attention to effectively writing and presenting data analysis. We will use business cases and blog, journal, and newspaper articles to link the course material to real world settings. We hope that you will enjoy business analytics as much as we do! Learning Objectives By the end of this class, you will: ● Acquire “statistical literacy,” meaning that you can interpret statistics frequently used in current events, industry reports, and so on; ● Distinguish between descriptive and inferential statistics, and apply skills such as data summarization, hypothesis testing, and regression analysis, using Excel; ● Apply the core concepts of probability to decision-making under uncertainty, including an introduction to simulation; ● Synthesize your knowledge with quantitative business cases; and ● Effectively communicate data analyses in written, visual, and oral formats. Friday Discussions Discussion sessions will be held each Friday, and attendance is mandatory. You will be expected to bring your laptops to discussion. We will use some discussion sessions to provide hands-on training in Excel to help you complete the cases. In the weeks before the midterm exam, you will take quizzes testing concepts taught in the course to help prepare you for the midterm exam. These quizzes are not graded but will help you, and us, determine your progress in the course. Credits Business Analytics I is a 3 credit course. This is comprised of a 50-minute lecture on Monday and Wednesday, and a 50-minute discussion on Friday. Between case work, Excel work, homework, and exam preparation, we expect you to spend (much) more than 2 hours a week outside of lecture/discussion on this class. Canvas We use Canvas for this course. The URL is: 306: Business Analytics I – Spring 2018 Page 3 of 7 Copyright © 2018 by Hessam Bavafa, Richard Crabb, and Anita Mukherjee Software We will make extensive use of Microsoft Excel, so please make sure you have the most recent version (available through the University at no cost to you) installed on your computer. Textbook There is no required textbook for this course. We will provide you the needed course content during lecture. If you wish to have another resource, however, we will provide you the mapping of the lecture content to relevant sections in the following recommended textbook. We will not assume that you have read the following text in preparing for cases or exams. Albright and Winston. Business Analytics: Data Analysis and Decision Making, 6th Edition (Note: the 5th edition also contains all the relevant content.) Hard copy is available at the bookstore, and copies are also on reserve at the Grainger library. (Some students also find the following textbook useful as an easy-to-follow resource: Gonick and Smith. The Cartoon Guide to Statistics. 1993.) Exams There are two exams in this course. The midterm exam will be held outside of class on the evening of Tuesday, March 20, from 7:00 – 9:00pm (room TBA). The final exam will be held on Wednesday, May 9, 5:05 - 7:05pm (room TBA), as per the University schedule. If you have a conflict with either of these times, you will need to fill out an online form on the course website (pay attention to announcements) well in advance. You may use a simple (non-graphing) calculator on these exams. Cases There will be two cases that involve application of lecture concepts and Excel analyses to business problems. You will work on these cases individually, and we will provide you with a dataset with structured guidelines for analysis. Each student will receive a unique, randomized subset of a larger dataset from a real business. Your case submissions will be graded on both the

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