Syllabus DSO510 PDF

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DSO 510: Business Analytics (Fall 2020)

Instructor: Mohammed Alyakoob Contact Info: [email protected] Course Website: blackboard.usc.edu Office Hours: By appointment (via Zoom)

Class Day and Time This class will be entirely online. Synchronous class sessions for Section 16305 will be held Mondays and Wednesdays from 11:00 – 12:20pm. Synchronous class sections for Section 16301 will be held Wednesdays from 6:30 - 9:30pm. All class session will be held via Zoom. The Zoom link is posted with your Blackboard course pages. Course Description Business analytics is the process of utilizing tools and techniques to turn data into meaningful business insights. This course provides students with foundational knowledge for business analytics, including strategies, methods, and tools. Students will obtain the necessary skills for defining business analytics for data-driven decision making and innovation as well as hands-on experience using analytics to solve real-world problems. While this course exposes students to a variety of analytics tools, the focal objective is to provide a managerial perspective on the usage and role of business analytics in progressive corporations. This course incorporates various analytics tools, including Python, SQL, API’s, Tableau, and Gephi. It is comprised of four modules, where each module provides students with an opportunity to obtain hands on experience regarding a technical aspect of the business analytics process. These modules are 1) Defining Business Problems and Obtaining and Organizing Data 2) Descriptive Analytics and Visualization 3) Predictive Analytics 4) Prescriptive Analytics. Each module also incorporates case studies and articles that address the impact of business analytics on specific domains. Therefore, while gradually accumulating the technical skills required to analytically examine business problems, students also obtain a managerial perspective on the importance of analytics across various domains. Importantly, this course is focused on the process of translating data analytics into meaningful business insights and outcomes.

Here is an overview of the technical skills students will acquire during this course: • • • • • • •

Hands on experience obtaining and organizing data from various sources, including API’s (Twitter, Google, etc.). Managing and querying data using Python and SQL. Present meaningful representations/visualizations of the data including graphs, plots, and geographic/spatial distributions to obtain insights. Conduct network analysis on social network data from Twitter. Analyze and interpret the results of regression analysis, text mining analysis, clustering, and other advanced statistical methodologies. Utilize cloud based artificial intelligence platforms (i.e., Microsoft Azure) and interpret and present the results. Obtain an understanding of the fundamental differences between prescriptive and predictive analytics and their role in driving business decisions.

Required Readings and Supplementary Materials 1. Journal Articles: Available for free through the USC libraries. (http://libguides.usc.edu/go.php?c=9231877 ). 2. Business Cases: Instructions for downloading business cases will be provided.

Course Notes This class is organized into 13 sessions, where each session consists of a week of class. Students are required to prepare for each session by reading the cases and/or articles assigned for that week prior to the class session. Please be prepared to actively participate in problem solving sessions and discuss the readings. The primary reading assignments are mentioned in the course schedule. Other reading assignments, if any, will be posted on the course website. The main communication channel for the course will be through Blackboard and emails. Periodic announcements will be sent through Blackboard, so please make sure to check for these. We will also use Slack for communication. Questions about assignments, projects, homework etc. can be asked on Slack and classmates with similar concerns will be able to observe the exchanges. Each student’s grade will be comprised of homework, assignments, participation, an analytics trends presentation, and a group project. Homework is to be completed individually while assignments can be done in pairs. Case write-ups are to be submitted at

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the beginning of the session in which a case is to be discussed. See below for more details regarding the grade breakdown as well as further details about each course requirement.

Grading Breakdown

Assignment Individual Homework Assignments Participation Analytics Trends Presentation Project TOTAL

% of Grade 25 25 10 5 35 100

A. Individual Homework There will be 3 individual homework to be completed throughout the semester. Each homework will be assigned to reinforce concepts, techniques, and methods covered in class. B. Assignments There will be two assignments to be completed. You may work with another student to complete each assignment. You do not have to work with the same person for each assignment. These assignments are geared toward applying and advancing the methods and techniques covered in class. Assignments will be generally more advanced and time-consuming than homework. C. Class Participation Class sessions will provide useful information – both for learning the topics covered in the course and for working on the project. We do not have a textbook and are dealing with versatile topics. Hence, student participation in class discussions is crucial because it introduces alternative viewpoints and helps clarify concepts for the class as a whole. Participation grades will be based on the quality of a student’s contribution to the lectures. Students are expected to read the cases in depth and be prepared to discuss the readings in class. The final participation grade will be determined solely at the discretion of the instructor.

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D. Analytics Trends Presentation Students will form groups and present a 5-7 minute overview of a recent topic or issue in business analytics. This may cover a recent use of business analytics by a specific company, a recent tool that has garnered attention by a specific industry, or other trending topics. Students are required to consult with the instructor to confirm the appropriateness of the topic and that another group has not already selected the topic. E. Group Project There is one team project. Detailed explanations of the project will be posted on the class website. You will form groups to work together on a real-world problem, with the approval of the instructor. Students are expected to substantially contribute to the completion of the team project in this course. The project gives students an opportunity to creatively think how big data analytics can be applied to a real business problem. There are four phases of the project and students are expected to sequentially submit project material. The following outlines the project phases and their associated percentages: 1) Define problem and outline the business insights to be obtained. Submit a one to two-page summary of the problem and why it is important. Outline the data sources that will be used to answer this problem. (5%) 2) Collect data and combine/query the data for the purposes of obtaining the insights prescribed in part 1. 3) Prepare descriptive analytics on the data and visualizations. Present these to the class using Tableau. Discuss the tools that will be used to answer the research question and obtain feedback from classmates and the instructor before conducting final analysis. (10%) 4) Final presentation and project submission. Present findings to the class and discuss the methodology and business insights obtained. Project submission should have a summary of findings and analysis. It should be clear and concise. Submissions that are not clear and/or difficult to understand will be penalized. All code and statistical analysis must also be submitted. (20%)

Assignment Submission Policy All content submission will be submitted through Blackboard. Late submissions will be penalized by 25%. Remember late is late, whether it is 1 minute or one hour, so please be sure to submit in advance of deadlines. A maximum of 24 hours will be allowed for late submissions (as mentioned, these will be penalized by 25%) after which the submission window will close and the submitter(s) will receive a 0.

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Course Schedule: A Weekly Breakdown * Class schedule may be modified during the semester. Please check the class website and emails before every class for announcements, assignments, and schedule changes.

Topics/Daily Activities

Readings and Homework

Deliverable/ Due Dates

Week 1: 08/17/2020

Introduction/Python Basics

Week 2: 08/24/2020

Data Collection and Organization

Week 3: 08/31/2020

Continued.

Week 4: 09/07/2020

Continued

Week 5: 09/14/2020

Descriptive Analytics and Visualization

Week 6: 09/21/2020

Continued

Week 7: 09/28/2020

Network Analysis

Week 8: 10/05/2020

Project Initial Presentations

Project: Initial Presentation

Week 9: 10/12/2020

Regression Analysis

Assignment 1 10/18/2020

Articles: • Big Data: The Management Revolution. Harvard Business Review. Article: • The Path to Prescription: Closing the Gap Between the Promise and the Reality of Big Data. Rotman Management Magazine. • 3 Common Mistakes that can Derail Your Team’s Predictive Analytics Efforts. Harvard Business Review. Article: • Data Analytics from Bias to Better Decisions. Rotman Management Magazine. Case: • Predicting Consumer Tastes with Big Data at Gap. Article: • Integrating Analytics in Your Organization: Lessons from the Sports Industry. MIT Sloan Management Review. Article: • How a German Manufacturing Company Set Up Its Analytics Lab. Harvard Business Review. Case: • UCB: Data is the New Drug. Article: • Better People Analytics. Harvard Business Review. Case: • Trust the Algorithm or Your Gut?

HW 1 09/06/2020

Project Part 1: 1-2 page submission 09/18/2020

HW 2 09/27/2020

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Week 10: 10/19/2020

Text Mining (NLTK)

Week 11: 10/26/2020

Prescriptive Analytics

Week 12: 11/02/2020

Prescriptive Analytics

Week 13: 11/09/2020

Final Project Presentations

Article: • 4 Analytics Concepts Every Manager Should Understand. Harvard Business Review Digital Articles Article • Your Biggest Social Media Fans Might Not Be Your Best Customers. Harvard Business Review Digital Articles.

HW 3 11/01/2020

Assignment 2 11/15/2020

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