Angelo State University MGMT 6303 Advanced Data Analytics Section D10 (online) Spring 2017 Professor: Rex Moody, Ph.D. Office: RAS 206 Phone: 325-486-6613 email: rex.moody@angelo.edu Office Hours Tuesday, Wednesday, Thursday: 1:00 p.m. - 4:00 p.m. and by appointment Dr. Moody will also be available to you for evening office hours / meetings if needed, either via phone, Web-Ex, or in-person. Please send me a note and we can easily set something up should we need to meet. You can also use the discussion forum within Blackboard to ask questions regarding the course and course content. The discussion board will be checked by Dr. Moody or our class coach Maureen Rajaballey at least once per day. You may also email questions directly to Ms. Rajaballey at: maureen.rajaballey@iconnect-na.com. Part of Ms. Rajaballey s role as a course coach is to answer your questions and work within the discussion forums during the term. In this context, Ms. Rajaballey is much like a teaching assistant for the course. Required Materials 1. Predictive Analytics by Eric Siegel, 2016, Wiley, ISBN 978-1-119-14567-7 You should be able to find this book on your favorite online book seller s site for $10 - $20 for a paperback copy. It seems like it is also available in different electronic versions, if you prefer. 2. Data Analytics Simulation: Strategic Decision Making This is available only through Harvard Business School Publishing, it is the lone item in a coursepack that can be purchased through this URL: http://cb.hbsp.harvard.edu/cbmp/access/57545070 3. Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations, by Scott Berinato This book is published by Harvard Business School Publishing. It is available in PDF form for $17.50 and is the lone item in a second coursepack that can be purchased through this (different) URL: http://cb.hbsp.harvard.edu/cbmp/access/57548664 If you prefer a printed, paperback version of this book, you can find it at your favorite online bookseller for around $22. If you buy it directly through the Harvard Business Review site it seems to cost $10 more. 4. Tableau Software Download We will also use Tableau Software during the term. You can download it for free at tableau.com.
Prerequisites and Special Requirements for the Course There are no prerequisites for this course. Required Reading Reading the assigned materials is required in this course. Other outside readings may also be provided and be required reading from time-to-time to enhance your learning in this course. Prerequisite Technical Skills and Course Technology There are no special technical requirements for this course beyond access to the Internet/World Wide Web and MS Office (which you can get for free through ASU). Students must have access to the Internet/World Wide Web, the Microsoft Office suite of software, and means to create video presentation submissions to complete this course. You should complete your written work for this course using MS Word (you can get it for free as an ASU student); we will also use PowerPoint in this course. There are several ways in which you can create your presentation submissions. These will be reviewed prior to the first submission due date. Lastly, we will use a software package called Tableau in the course. Tableau can be downloaded for free at www.tableau.com. All needed technological resources for this course, except Tableau, are available in ASU computer labs. Technology Support Blackboard and university computer lab technical support is provided by the university s Technology Service Center by calling 325-942-2911 or 1-866-942-2911 or by email at helpdesk@angelo.edu. Assistance with Course Pack purchasing for the materials available through Harvard Business School can be obtained directly from the publisher s customer service department at 800-545-7685 or custserv@hbsp.harvard.edu.
ASU Catalog Course Description This course explores data collection and analysis techniques commonly practiced in business today. Topics include primary and secondary data collection techniques, analysis of collected data, and associated ethical concerns. Course Objectives The objectives of this course are to: Introduce students to the practice of data analytics and the tools that are commonly used by data scientists when conducting data analytics. Have students make strategic managerial decisions based on data, much like a manager would. Instill in the students a knowledge of the science behind good data visualizations. Enable students to create meaningful data visualizations (charts). Enable students to use Tableau to present data in meaningful ways. Student Learning Outcomes By the end of this course, students should be able to: Define the concept of predictive analytics and the role it plays in the world today Describe techniques used in the predictive analytics / data analytics field to help managers make sense of data and make better decisions Propose creative ways you might explore data with predictive analytics based on examples of what companies and organizations have done in the past in relation to predictive analytics and predictive analytics tools Explain how data analytics can be used in real managerial situations to make decisions Discuss the positive and negative issues of using data to make managerial decisions Discuss the science behind creating good data visualizations Be able to read and decipher charts Create charts that will impress and in the correct circumstances persuade Use Tableau Software to create good charts for a variety of purposes Course Format Students will gain their knowledge and learn about business research methods through readings, recorded lectures, and practice with software and other tools. Students will then apply their knowledge in individual presentations, exercises, and assignments. Methods of Assessing Learning Outcomes Your learning in this course will be assessed based on your performance in three areas: Written assignments and exercises related to the course material. Recorded video presentations on the material covered. Participation in online class discussions.
Assignments and Exercises Through out the term written assignments, exercises, and recorded video presentations will be used so students can show the knowledge they have gained through studying the course material. All of the assignments in this course are individual in nature. I expect your work in this course to be thoughtful and at a level becoming a graduate business student. Assignments will be posted in Blackboard under the Assignment tab by Thursday during the week noted in the syllabus. All assignments will be due on the following Wednesday; you should turn all assignments in via the Assignment link within Blackboard by the specific deadline noted on the assignment. All written assignments should be prepared using MS Word. Your video assignments can be completed in a number of different ways, which will be covered early in the term, prior to the due date fo the first video assignment. Your time commitment for this course will be between five and six hours per week. s You are expected to participate in online class discussions in this course. Suggested topics of discussion and discussion starters will be offered each week related to the class material for the week. These discussions will be monitored mainly by Ms. Rajaballey, though Dr. Moody will also be chiming in from time-to-time as necessary. You should ask questions that you have or start discussion threads as you see fit within the discussion forums. By asking your questions in the forums, all students will be able to see the answers and if so desired can offer their opinion on the question. Overall, the expectation is for meaningful, professional-type discussions to occur on an on-going basis during the term. Course Grades Your semester grade will consist of the following components: Eight s @ 50 points each: 400 points Recorded Presentation on Predictive Analytics: 200 points Performance on Simulation: 50 points Data Visualization Recorded Presentation: 200 points Participation in s: 150 points Total 1,000 points The following cutoffs (based on points earned) will be used to determine final grades in MGMT 6303: A 900-1,000 B 800-899 C 700-799 D 600-699 F Below
Course Policies Absenteeism Since this is an online class, there is no attendance policy connected to the course. Your regular participation in online class discussions and submission of work for the course indicates your attendance. Professionalism Students are expected to conduct themselves professionally in all matters related to this class. This means students should prepare all assignments in a professional manner and act professionally when participating in class discussions. Inappropriate comments directed toward others in class and/or inappropriate comments in assignments will not be tolerated. Unless otherwise noted, written assignments should be word processed and be free of spelling and grammatical errors. Deadlines/Late Assignments Assignments will be due on the dates and times given when the work is assigned. Late assignments will not be accepted, unless arrangements have been made with the instructor prior to the assignment due date. Plan ahead and be prepared to turn your assignments in when they are due. You will always have at least six days to complete your assignments. Academic Honesty and Integrity Angelo State University expects its students to maintain complete honesty and integrity in their academic pursuits. Students are responsible for understanding the Academic Honor Code, which is contained in both print and web versions of the Student Handbook. In essence, the willingness to cheat undermines our purpose at the university. In general, all students are expected to conduct themselves in this course in a manner consistent with the University Honor Code policy which is at: http://www.angelo.edu/forms/pdf/honor_code.pdf Cheating in any form will not be tolerated in MGMT 6303; the work you hand in must be your own. Please keep in mind that plagiarism (presenting another person s work as if it is your own) is considered a form of cheating. If you are caught plagiarizing in any way on your written assignments, your punishment, at the least, will be a grade of zero on that assignment. Major cases of plagiarism will be cause for a student s course grade to be an F. Policy on Disabilities Angelo State University is committed to the principle that no qualified individual with a disability shall, on the basis of disability, be excluded from participation in or be denied the benefits of the services, programs, or activities of the university, or be subjected to discrimination by the university, as provided by the Americans with Disabilities Act of 1990 (ADA), the Americans with Disabilities Act Amendments Act of 2008 (ADAAA), and subsequent legislation. The Student Life Office is the designated campus department charged with the responsibility of reviewing and authorizing requests for reasonable accommodations based on a disability, and it is the student s responsibility to initiate such a request by contacting the Student Life Office, Room 112 University Center, at (325) 942-2191 or (325) 942-2126 (TDD/FAX) or by e-mail at Student.Life@angelo.edu to begin the process.
Policy on Religious Observances A student who intends to observe a religious holy day that will interfere with their course work should make that intention known in writing to the instructor prior to the date of the religious observance. Your instructor will do everything in his power to accommodate the student so that they are not negatively impacted in the class by participating in the religious observance. Incomplete Grades Incomplete grades will only be given for legitimate circumstances. In order to be considered for an incomplete grade, the student should contact the instructor immediately after they realize they may not be able to complete a substantial portion of the course as scheduled. Grade Appeal Process A student who believes that he or she has not been held to appropriate academic standards as outlined in the class syllabus, equitable evaluation procedures, or appropriate grading, may appeal the final grade given in the course. The burden of proof is upon the student to demonstrate the appropriateness of the appeal. A student with a complaint about a grade is encouraged to first discuss the matter with the instructor. For complete details, including the responsibilities of the parties involved in the process and the number of days allowed for completing the steps in the process, see Operating Procedure 10.03 at: http://www.angelo.edu/content/files/14196-op-1003-grade-grievance. Add/Drop Dates To view information about how to drop this course or to calculate important dates relevant to dropping this course, you can visit: http://www.angelo.edu/services/registrars_office/course_drop_provisions.php. The last day to drop a class during the Spring 2017 term is March 31.
Course Schedule Module 1: An Introduction to Predictive Analytics For this module we will read, discuss, and complete assignments related to the book Predictive Analytics. This section of the course is intended to be an introduction to predictive analytics and how it works and affects us all in business and everyday life. This is not a how-to book, you will not become an expert data scientist in this section of the course, but by the end of this module you should have a solid appreciation of what Predictive Analytics is and how companies and other organizations use it to gain advantages in the marketplace. Learning Goals After completing this module, students should be able to: # Define the concept of predictive analytics and the role it plays in the world today # Describe techniques used in the predictive analytics / data analytics field to help managers make sense of data and make better decisions # Propose creative ways you might explore data with predictive analytics based on examples of what companies and organizations have done in the past in relation to predictive analytics and predictive analytics tools Week Of Materials Topic Graded Items Jan. 18 Note that the weeks in this course start on Wednesday Introduction to the course and Data Analytics Jan. 25 Predictive Analytics: Introduction, Chapters 1 & 2 Deployment and Ethics of Predictive Analytics Readings should be completed by the date shown to the left each week Feb. 1 Predictive Analytics: Chapters 3-5 Data, Modeling, and Ensembles Feb. 8 Predictive Analytics: Chapters 6-7 Question Answering and Uplift Feb. 15 Predictive Analytics Mini-Case Studies and related readings Student Presentations Presentations Recorded Video Presentation
Module 2: Practicing Data Analytics In this second module we will practice the type of situation a manager may be faced with through the Harvard Business School Data Analytics Simulation: Strategic Decision Making. Specifically, in the simulation, you will act as a marketing manager using data to make strategic decisions for your product, a laundry detergent. Learning Goals After completing this module, students should be able to: # Explain how data analytics can be used in real managerial situations to make decisions # Discuss the positive and negative issues of using data to make managerial decisions Week Of Materials Topic Graded Items Feb. 22 Harvard Business Simulation Running the simulation Mar. 1 Harvard Business Simulation Simulation results Simulation Results
Module 3: Data Visualization and Presentation In this final module of the course we will explore how to create meaningful data visualizations. We will first explore the theories and best practices of creating data visualizations. Then we will learn about the Tableau Software package and practice using it for a semester culminating presentation. Learning Goals: After completing this module, students should be able to: # Discuss the science behind creating good data visualizations # Be able to read and decipher charts # Create charts that will impress and in the correct circumstances persuade # Use Tableau Software to create good charts for a variety of purposes Week Of Materials Topic Graded Items Mar. 8 Mar. 15 Mar. 22 Mar. 29 Apr. 5 Good Charts, Part 1 (Chapters 1 & 2) Spring Break Good Charts, Part 2 (Chapters 3& 4) Good Charts, Part 3 (Chapters 5-7) Good Charts, Part 4 (Chapters 8 & 9) Understand: History of Data Visualization and the Science of How We See Create: Four Types of Charts and A Simple Framework for Making Better Charts Refine: Refine Charts to Impress and/or Persuade, Blurring the Truth (persuasion or manipulation?) Present and Practice: Present to Persuade, Practicing Reading and Making Charts (Visual Crit) Apr. 12 Tableau Software Creating Data Visualizations with Tableau Apr. 19 Tableau Software Creating Data Visualizations with Tableau Apr. 26 Tableau Software Creating Data Visualizations with Tableau Tableau Visualization Tableau Visualization May 3 Student Presentations Presentations Recorded Presentations May 10 Finals Week Used for catch-up if required