Professional MBA Program COURSE: CIS 503: Decision Making with Data Analytics TERM: 2022 Spring A | Jan 8 - Feb 28, 2024 Monday, 8:00 am -12:00pm (SLN: 16896) Classroom: MCRD 276 FACULTY INFORMATION Professor: Nitin Walia, Ph.D. Office: BAC 633 Virtual Office Hours: Wednesday 11:00 am - 2:00 pm (AZ Time) Via Zoom or by appointment Zoom Room: https://asu.zoom.us/my/nwalia1 E-mail: nwalia1@asu.edu Office Phone: 602-496-5416 TA Information: COURSE DESCRIPTION This course presents frameworks and approaches to use data analytics to recognize patterns in data, utilize them to improve decision making, and systematically transform business processes for performance gains. Students will be trained to think critically on a range of issues including identifying potential opportunities for envisioning data-analytic solutions to improve decision-making, identifying appropriate analytical modeling techniques, deploying the results of analysis into a business process, and, finally, measuring the improvement in the business process performance with the help of the right performance metrics. The course also critically examines how data analytics fits in an organization’s competitive strategy and explores how data analytical capabilities lead to competitive advantage and, in some instances, influences business strategy. Readings include decision-making challenges faced by managers in data-rich environments. Students will get an immersive experience in the mindset needed to foster and nurture analytical thinking and institute persistent, systematic change at the enterprise level. W. P. Carey School of Business Learning Goals The W. P. Carey School of Business has established the following learning goals for its graduate students: 1. 2. 3. 4. Critical Thinking Communication Discipline-Specific Knowledge Ethical Leadership or Global Leadership Items in bold have significant coverage in this course. Course Learning Outcomes The course builds on the prior courses in the curriculum and shapes a holistic view of the field. At the end of this course, students should be able to answer the following questions: ❖ Evaluate the decisions that are critical to an organization and apply analytical thinking to decision-making o Identify the critical decisions. Assess the factors that go into making such decisions. o Design the roles, behaviors, and methods the organization should use for analytical transformation. Institutionalize the analytically improved decision-making processes. ❖ Learn and apply analytical techniques that can be embedded in decisions o What are the fundamental analytical models (e.g., for supervised/unsupervised learning)? Identify appropriate analytical models to transform decision processes. o Embed the results of analytical models into business processes to improve business performance. Measure the impact of analytical transformation. Test and learn through experimentation. ❖ Build ever-evolving data analytics capability within the organization o Why do evidence-based management and how to practice it? How to build an evidence-based decision-making culture? o How to be ready for the ever-evolving landscape due to emerging technologies (such as Big Data and the Internet of Things (IoT))? Recognize the need for organizational agility. Identify value-creation opportunities via new business models. Teaching Methodology This course follows the “flipped classroom” approach to learning. Before coming to each class, you will typically complete these activities: 1. Read the assigned readings. Note the recommended reading levels: Intense, Moderate, and Light. 2. Watch assigned recorded lectures and answer the embedded questions. These cover basic concepts, frameworks, and models that we shall build upon in class. 3. Complete the assigned homework and submit it online. Please check canvas for due dates 4. Prepare to discuss the assigned readings and recordings. Make notes for yourself. In each class, we will typically conduct the following activities: 1. Discussions: The professor will facilitate discussions to address the questions from the readings, with students contributing to most of the conversation. 2. Professor-Led Presentations: Professor will discuss material to complement the recorded lectures and discuss the application of concepts to real-life situations. 3. In-class Activities: Students will be asked to work in groups on topics of the day, do problem-solving, generate ideas, write short answers to questions, etc. 4. Student Team Led Presentations/Discussions: In some classes, student teams will make a presentation in front of the class to provide a deeper understanding of a topic. Each class will be highly interactive and driven by student participation. The effort you put in preparation will enable you to extract the most out of the class. How to be Successful in this Course For each class, you will read articles (including case studies). We have recommended a level of reading effort for each article (light, moderate, intense). Reading at a light level means reading in one quick pass. Read and understand the big picture and the vocabulary. This is background reading or an article that is supplementary to the main topics. We may refer to these articles in class, but we may not discuss them at length. Reading at a moderate level means giving the article one solid end-to-end pass, highlighting sections that you feel are important, making notes in the margins, etc. We shall discuss this material in class. Reading at an intense level means reading in an immersed manner and then going back and rereading important portions. Read the posted homework and/or the discussion questions, discuss in a study group, if needed, and make reference notes for answering the discussion questions. Be prepared to engage in a deep discussion in class beyond the factual material in the article. Required Textbook and Other Materials Required Textbook: Data Science for Business, by Foster Provost & Tom Fawcett. O’Reilly, 2013. http://data-science-for-biz.com/ Harvard Coursepack Purchase the Coursepack here: https://hbsp.harvard.edu/import/1130131 Other Free Resource Materials • “Data Mining Primer” – free resource reading • “Customer Relationship Management at Capital One”, Uday Kulkarni, mini case study • “Mining Online Reviews to Uncover Consumer Brand Engagement,” Uday Kulkarni, Admit V. Deokar, and Haya Ajjan. • “Sentiment Analysis,” Uday Kulkarni. Additional readings, video clips, etc. will be made available on the course website as and when needed and may be part of the required preparation for the class. Software • Tableau: Free license key is provided on the course webiste. Refer to the Tableau Tutorial page in Canvas. You can also apply to get a free student license. Details are in the Tableau tutorial in Canvas. • Azure ML: Free usage is available to ASU students with asurite sign-in. Refer to the Azure ML page in Canvas. It is considered a violation of academic integrity to utilize course materials that are illegally sourced. Please ensure that you are ordering and paying for your own materials as outlined in the ordering instructions. Communicating with Your Instructor and Classmates Announcements: Communications such as Welcome email, posting of grades/general feedback, agenda changes, etc., that are meant for the entire class will be posted on the class Canvas website as announcements. Please make sure that your Canvas website notifications are set to inform you of announcements via your email. Emails from me to you: We will use Canvas email to communicate with you officially. If you have auto-forwarding set up to any other email account, please ensure that the forwarding mechanism works in a timely manner; if it doesn’t, please check your ASU email account regularly. Emails from you to me: Please use my ASU email nitin.walia@asu.edu to send personal emails to me directly. Please remind me if I do not respond within 24 hours. I would appreciate if you do NOT use Canvas email to send individual emails to me. [Reason: I have to log into Canvas every time to reply to your email. This is especially burdensome when we may be having a back-and-forth email conversation. Moreover, I can respond to your direct email more quickly from my phone]. Team-related emails: Please copy all your team members on any email that is team-related and send it to my e-mail directly. That way, I can use “reply-all” to respond to all team members and every team member is equally informed. Zoom related issues : No Zoom link is offered for attending the class, except for cases pre-sanctioned by the Graduate Programs Office (GPO Student Responsibility Policies . As Per GPO “Everyone is required to attend immersion classes in person. Zoom attendance for immersion programs is not an option. An online session is not a substitute for an immersion session in most cases.W. P. Carey Graduate Programs do not openly offer Zoom links to students, nor promise to record classes. ” see more details under GPO Student Responsibility Policies COURSE GRADING OVERVIEW Course grades will be based on the following deliverables and weights: Grading Criteria Weight Homework: Seven 20% Quizzes: Five 30% Attendance and class discussion contribution 5% PlayPosit recorded lectures 5% D 12% Data visualization project* Data mining project* 12% Final team project* 16% * *Team grades - 50% based on peer evaluations by team members D Data visualization project has a team and individual component ASSIGNMENTS AND ASSESSMENTS Homework: We have designed the homework assignments to test your ability to educate yourself from the assigned readings. They build the foundation for in-class activities and consist of discussion posts that you will individually complete and submit online. The grade is mainly based on the clarity and depth of the thought process and the quality of the writing. The effort you put into this task will enable you to contribute actively in class. Quizzes: We will have five online quizzes that are 60 mins in duration. The quizzes are designed to test your ability to educate yourself from the assigned readings and your grasp of in-class presentations and discussions. The quizzes are open-book and open-notes. The quizzes are individual assignments. Do not share any information related to the quizzes with your classmates or anyone outside the class. Attendance and Class discussion contribution: Class discussions are opportunities for you to demonstrate your understanding of the issues and questions posed in class, relating them to other material (e.g., readings, lectures, work experience, etc.), offering synthesis and evaluation, responding thoughtfully to others, and moving the thinking of the class forward. The grade is based on your presence (online or in person) and the consistency and quality of your contributions throughout the course. Playposit recorded lectures: Professor Kulkarni has recorded a series of video lectures for our course that you will review prior to class. These lectures also contain short "check your understanding" multiple-choice questions covering high-level concepts from the recorded lectures. Details regarding PlayPosit can be found herehttps://knowledge.playposit.com/article/212-the-student-experience-3-0 Data visualization project: You will complete a visualization team project using a data set of your choice from those on the course website (or your own) with Tableau software that you will install on your own machines. The grade is subjective and based on the quality with which the requirements are completed. You will also have an individual assignment where you will evaluate the other teams’ submissions. Data mining project: You will complete a 2-part data mining team project using the Azure ML cloud-based software. You are encouraged to work independently to learn to use the software and then combine your knowledge to build the project deliverables as a team. Final team project: For the final team project, each team will propose a creative, practical, and useful "analytical" enhancement to a business process that you are familiar with in a company of your choice. The company you choose would preferably be one where one of you works (or has worked) or your team is familiar with. Your team will present the proposal in front of the class. The presentation will be evaluated by the instructor and by all the other teams. Assignments turned in late will not be accepted and a score of zero will be assigned. Grading Scale Total grade points will be calculated as a percentage, and letter grades will be assigned based on the scale below: 97% or above = A+ 77% - 79.99% = C+ 94% - 96.99% = A 70% - 76.99% = C 90% = 93.99% = A- 60% - 69.99% = D 87% - 89.99% = B+ Below 60% = E 84% - 86.99% = B 80% - 83.99% - BPlease remember that you must earn above a C in every class in the Professional MBA program to avoid Academic Probation. If you are concerned about your grade in this class, contact the instructor as soon as possible to get extra help and get back on track. It is better to reach out early on, as there isn’t a lot that can be done after the work has been graded. Important Grading Policies ● ● ● ● You will not be permitted to use the group work to boost your grade if your individual performance is not adequate. Your individual performance is, therefore, paramount to your learning and your grade. The maximum amount that your group work can enhance your grade is one full letter grade. I also reserve the right to allocate scores differentially within groups based on peer evaluations. You may not allow other group members to carry your performance. You must have a passing average on individual work and quizzes to pass the class. Once points are posted for any exam, assignment, or extra credit activities on Canvas, you will have one calendar week to notify me of any grade discrepancies or questions about your grade. After the one-week period has passed for each grade component, the results are final and unchangeable. Late work will not be accepted without prior approval [from the instructor] and proper documentation if needed. Arrangements for make-up quizzes and exams should be made BEFORE the quiz/exam and only with the instructor’s approval (e.g. medical emergencies, unavoidable conflict). In other words, if you are unable to take the test at the arranged time, you must contact me BEFORE the test time. Unexcused make-up tests will be subject to a 25% or higher reduction in the score. Medically excused missed quizzes and exams will not incur any reduction. Professional MBA Tutoring Support Please contact Program Ops for tutoring support. COURSE SCHEDULE Please see the posted Schedule in your Canvas course site. W. P. CAREY PROFESSIONAL MBA EXPECTATIONS, RESOURCES, AND POLICIES To access important W. P. Carey policy information, students should go to: https://sites.google.com/asu.edu/wpc-online-mba-expectations These policies are standard for every Professional MBA class at W. P. Carey, and contain information about Student Conduct: W. P. Carey Professionalism Policy, W. P. Carey Honor Code, and Threatening Behavior Policy Student Resources: Tutoring Support, Technical Support, and Professional ● MBA Program Support Accommodations: Religious, Disability, and University-Sanctioned Activities ● Course Policies: Offensive Material, Course Copyright, and Course ● Feedback Discrimination Policies and Title IX ● ● GPO Policies & Guidelines for Graduate Students: https://docs.google.com/document/d/16e2jNj6g0QsQRjHyvTvZ1efVL404j_4dIKwGqox9eWY/ edit Students are expected to review the W. P. Carey Professional MBA policies at the start of every term, as this document is updated regularly.
