UNIVERSITY OF SOUTHERN CALIFORNIA



UNIVERSITY OF SOUTHERN CALIFORNIAMarshall School of BusinessGSBA 524 – Managerial statistics Professor: Robertas Gabrys, PhDOffice: Bridge Hall 401 OOffice Hours: Tue 11:00 AM – 12:00 PM, Thu 1:00 PM – 2:00 PM or by appointmentEmail: gabrys@marshall.usc.eduCourse DescriptionData are everywhere! Companies routinely collect large volumes of data on customer profiles, point of sales transactions, and operating performance at different units. How do companies use these data to make effective financial, marketing, and operational decisions? How do organizations covert these data into business intelligence and insights? This course will give you the tools and methods to answer these questions, providing you with a unique competitive advantage in an increasingly data-centric global business environment.The goal of this course is to teach students how to convert raw data into actionable data that yields business intelligence and insights. We will also teach students how to build sophisticated models from raw data, and how to use these models to make effective business decisions. This is an integrative Capstone course that combines information technology, statistics, and decision theory, with applications to other business disciplines such as Finance, Management, Marketing, Operations, and Real Estate. The course will integrate the concepts that you’ve learned in other core classes.More specifically, we will investigate the following modules:Exploratory Data Analysis: How to convert raw data into a useful format for business analysis?AB Testing: How can we combine data and experimentation to incrementally improve our business model? Data-Driven Finance: Pricing of Real Estate via Linear RegressionData-Driven Marketing: Targeting and Segmentation via Logistic Regression and ClusteringData-Driven Operations: Optimal Allocation of Resources via Linear ProgrammingInstructional MethodsThe class will consist of lectures, quizzes, case-based class discussion, and computer lab work. Each case will start with one or two weeks of lectures followed by one or two weeks of compute lab work, and a quiz.Learning ObjectivesAt the end of this course, you will be able to:Explain in your own words the key ideas behind fundamental techniques in data analytics, including dashboarding, classification, clustering and AB-testingIdentify new opportunities to use these techniques across business domains to guide decision-makingConfidently apply these techniques to novel problems using a combination of Excel and JMPFormulate and communicate actionable business recommendations based upon your analysis, including its limitationsCritically assess the validity of analytics-based recommendations in the context of specific business decisionsPlease see the appendix for alignment of these goals with the Marshall Learning Objectives.Case Assignments: During the course we will analyze 5 case assignments. The case description will be posted on Blackboard. These cases are extremely important in learning the materials in class. Each of the 5 cases will address different skills and techniques in analyzing data. We will have in-class discussion of each case and work through parts of the case assignment during the computer lab session. Thus, it is important that you read the case materials before the lab session. You can earn participation credits by submitting a half-page discussion of the case via Blackboard by 1pm on the day of the lab. The half-page discussion should address the case questions listed in the course outline. The final case assignment is usually due one week after the lab session. Below is the guideline for completing your case assignments:Answer the questions that you are asked clearly and concisely. Some questions will ask for specific numbers and calculations. To receive full credits, you must show your work and calculation! You can write out your calculation in pens/pencils and stapled it to your final case submission.For questions that ask for charts and graphics, you must print out the graphics from Excel or JMP, and include them in your report. Please make sure to format your chart and graphics properly. Your scores on each assignment will depend on the quality of your submission.There will be some questions that ask you to assess and interpret the results of the model. In this case, you will need to provide appropriate outputs from the statistical analysis. When presenting the output of the statistical analysis, please be careful to format them so that it is easy to read and follow your logical arguments. Once again, your grade will depend on the quality of your submission. Finally, there will be questions that ask for you to identify the business insights that you obtain. Please state your answers briefly and concisely! Long-winded answers tend to receive lower scores. Case assignments are due in-class on the indicated due date. Late submissions are not puter Lab using Excel and JMP: Computer lab is an integral part of this course. We will have at least one lab session for each of the 5 case assignments. During the lab session, we will discuss the case in details, work through part of the required analysis, and provide guidance on how to complete the remaining questions in the case assignment. Thus, it is very important that you attend lab sessions. Course Notes: We will use Blackboard for all assignments, course materials, and announcements. Please check the Blackboard site and your email daily. If you would like hard copies of any course materials, it will be your responsibility to print them out prior to class.Working with software in the computer lab is an integral part of this course. We will have at least one lab session for each case assignment. During these sessions, we will discuss the case and practice using software. Your quizzes and assignments (see below) will often require you to use this software. Thus, it is very important that you attend and actively participate in lab sessions. Discussing homework assignments, pre-class preparation, and pre-class assignments with a partner or study-group is permitted and highly encouraged. Your peers are now and will always be your best resource to learn. However, each student is required to prepare, write-up, and submit her or her own solutions independently, including computer work. Collaboration of any sort on quizzes and exams is prohibited and will result in a zero on that quiz/exam and the appropriate University-level authorities to be notified. See also the Marshall Guidelines on Academic Integrity below.Case Presentation: There will be in-class presentations for each of the 5 case assignments, which will be done in teams. Teams should be formed by the beginning of Session 3. Each team will consist of 4 - 5 students. Once I receive the list of teams, I will assign each team one case assignment. Each team will then prepare a 10-minute PowerPoint presentation (and Excel files if applicable) for its assigned case. The presentation should summarize the key analyses and results for each of the case in a compelling and concise fashion. This is also an important opportunity for each team to extend their analyses beyond what is asked in each case assignment. Based on the quality submissions, I will select one team to present in-class. The presentation date will be arranged once the teams have been formed.Quizzes: At the end of each case, we will have a short in-class quiz to check your understanding of the materials. The questions on the quizzes will be a straightforward application of the data analysis technique that you have just learned. The data for the quiz will be provided in-class.GradingThe course grade, which will be curved, is based on a midterm, a cumulative final exam, in-class quizzes (there will be four quizzes, but only the best three will count toward the course grade), case assignments, and class participation, according to the following weights:Pre-Class Assignments 5%Participation 5% Quizzes 30%Homework15%Final Case Project15%Final Exam30%All exams/quizzes are closed books. You are allowed to use one double-sided crib sheet (8.5x11) on each quiz/exam. No make-up exams or quizzes are offered – accordingly, all quizzes must be taken on their assigned date and in the section in which students are registered. AssignmentsPre-Class AssignmentsPlease note that it is impossible to contribute to the learning environment if you are unprepared. For some sessions, short readings and videos will be distributed prior to class. With each reading or video, there may be a short pre-class assignment. These pre-class assignments will be very easy provided you have done the reading or watched the video. These pre-class assignments should be submitted via BB prior to class. Each case will have an associated pre-class assignment. These assignments require you to think about the business context of the case before the class we start working on the case. Please submit your responses to the questions via BB before class, and come prepared to discuss the case in detail during class. Class sessions will focus on using analytics techniques to guide the decision-making process and ultimately formulating a cogent recommendation.Class Participation One of the key learning outcomes of this course is to develop the ability to effectively discuss analytics techniques and communicate recommendations based on these techniques. Consequently, class participation is critical. Your participation is evaluated on the quality of your contribution, insights and four participation assignments that you will submit on blackboard. I will make every effort to call on as many students who wish to speak up as possible to provide a fair chance for contributions. QuizzesA second key learning outcome of this course is to develop the ability to confidently apply the analytics methods taught with software. Quizzes support that outcome, asking you to complete a straightforward application of data analysis techniques learned in class to new data; this will require using the computer lab. There will be five quizzes—one each for Basic Excel skills, KPIs and Dashboarding, AB testing, Classification, and Clustering.All quizzes are closed book and no Internet access, but WILL involve software in the computer lab. You are allowed to use one double-sided crib sheet (8.5x11) on each quiz. Crib sheets cannot be shared. No make-up exams or quizzes are offered – accordingly, all quizzes must be taken on their assigned date and in the section in which students are registered in the computer lab. HomeworkHomework assignments mirror the cases we explore in the lab and provide an opportunity for you to apply your skills to a new business problem. In many ways, these assignments are a good example of the kinds of analytics work you may expect to do in your first job out of Marshall.Answer the questions that you are asked clearly and concisely. Some questions will ask for specific numbers and calculations. To receive full credits, you must show your work. In some cases, you may wish to include a chart or graph. Please make sure to format it appropriately. Your scores on each assignment will depend on the quality and clarity of your submission. Finally, there may be questions that ask for you to make business recommendations based on your insights. Persuasive arguments tend to be brief. Long-winded answers often receive poorer scores. Final Case ProjectStudents will work in teams of four or five students to analyze a case. This case will require you to apply a variety of data analytics you’ve learned throughout the semester to a complex problem in promotional pricing and formulate actionable recommendations. Your project will involve a short write-up summarizing and justifying your recommendations, a 10-15 minute presentation to the class of your findings, and providing constructive feedback on other team’s presentations and analyses. Details of the case and requirements for the project, including grading expectations, will be distributed later in the semester. Final ExamThe final exam will be cumulative. It will involve both written and computer portions. All quizzes/exams are closed book and no Internet access. You are allowed to use four double-sided crib sheet (8.5x11) on the final exam. Crib sheets cannot be shared. The Final exam date and location will be announced shortly on BlackBoard and in class. It may differ from the date announced on the university web page, because it will require using the computer lab. Notice on Academic IntegrityDiscussion regarding case assignments is encouraged; however, the final write-up of each case assignment should be of your own. The use of unauthorized material, communication with fellow students during an examination, attempting to benefit from the work of another student, and similar behavior that defeats the intent of an examination or other class work is unacceptable to the University. It is often difficult to distinguish between a culpable act and inadvertent behavior resulting from the nervous tensions accompanying examinations. Where a clear violation has occurred, however, the instructor may disqualify the student's work as unacceptable and assign a failing mark on the paper. There may be additional penalties, including failing the course, in accordance to the university policies, as listed in the SCampus. For Students with DisabilitiesAny student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to your instructor as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m. - 5:00 p.m., Monday through Friday. The number for DSP is (213) 740-0776.Course Disclaimer This syllabus is an invitation to students to engage in an exciting and interactive study of data analysis in decision making. The intention of the BUAD 425 team of instructors is to provide you with information, offer practice with skill sets, and enhance your capacity to model large datasets and translate your model to sound decisions. The learning environment will be collaborative and supportive; we will learn from one another both in and out of the classroom. To that end, modifications to this syllabus might be warranted as determined by the instructors as we assess the learning needs of this particular class of students. COURSE OUTLINEModule 1: Exploratory Data AnalysisSession 1: Introduction to the Applichem Case and Data Analysis TechniquesQuestions: What is data analytics? Why data analytics?Learning Outcomes: The purpose of this lesson is to introduce the structure of the class. You will discover that the tools and techniques learned in this class can be applied to many businesses and organizations. Define and recognize opportunity to apply data analytics in real-world situationsUnderstand how companies organize and structure their dataIntroduce the Applichem Case and how to use Excel to evaluate key performance indicators (KPI)Session 2: LAB: Data Analysis in ExcelQuestions: How do we convert raw data into a usable format in Excel? How to analyze data and evaluate key performance metrics in Excel? How to create a dashboard?Learning Outcomes: You will develop familiarity with Excel as a tool for data analysis, evaluation of performance metrics, and creation of dashboard. We will work through the datasets for Case #1 for Applichem. Understand how to import raw data into ExcelLearn how to format data in ExcelAnalyze data in Excel using pivot tablesCreate charts and dashboards in ExcelModule 2: Data-Driven Finance: Pricing of Real Estate via Linear RegressionSession 3: Introduction to Linear Regression and JMP Questions: What is Linear Regression? Why? How do we use it to answer business questions? Learning Outcomes: Linear regression is one of the most powerful and commonly used tools in data analytics. You will learn how to use apply this technique to answer business questions, and study its underlying assumptions. We will also show to use JMP software to fit a linear regression model to data.Understand the linear regression modelLearn how to fit a linear regression model to the data using JMPInterpret the result of model and understand the statistical outputsRecognize the applications of linear regression model to business applicationsSession 4: Advanced Linear Regression Questions: How to select the right regression models? How to assess the model’s validity? How to transform variables? Learning Outcomes: Linear regression is a powerful tool for data analysis. You will learn how to assess and choose the most appropriate model for the business applications, identify outliers, and variables transformation. Select the appropriate regression modelAssess the model’s validityTransform variables to fit within the linear regression frameworkQuiz #1: Data Analysis in ExcelSession 5: LAB: Pricing of Real Estate via Linear RegressionQuestions: How to use linear regression to price a potential real estate location?Learning Outcomes: You will apply the theory of linear regression to price real estate properties, and help Applichem to determine the most effective location for its new plant. Apply linear regression to a problem in real estate finance, using datasets for Case #2 for Applichem.Predict the cost associated with each propertyIntegrate the result of the regression model to help Applichem decide on the most effective location.Module 3: Data-Driven Marketing: Targeting and Segmentation via Logistic Regression and ClusteringSession 6: Introduction to Logistic Regression and Decision TreeQuestions: What are logistic regression and decision tree? How to use logistic regression and decision tree for target marketing?Learning Outcomes: Logistic regression and decision trees are powerful tools for classification. They have many business applications including estimating likelihood of sales, target marketing, and credit scoring.Understand logistic regression and decision treeRecognize business application where these tools can be appliedLearn how to fit a logistic regression and decision tree to the data using JMPSession 7: Advanced Logistic Regression and Decision TreeQuestions: How to select the appropriate regression model and decision tree? How to assess the validity and accuracy of logistic regression models? How to incorporate the results of these models in business decisions? Learning Outcomes: We will learn how to select the appropriate regression model and understand how to assess the predictive power of the logistic regression model. We will also discuss application to predictive marketing. Understand the relevant issues in selecting logistic regression modelsRecognize the important of the Receiver Operating Characteristic (ROC) associated with the logistic regression modelUse the information from the ROC curve to make an informed business decisionQuiz # 2: Linear RegressionSession 8: MidtermSession 9: LAB: Online Marketing and Targeting of Smart Partyware CustomersQuestions: How to apply logistic regression and decision tree to improve the customer response rate in a direct marketing campaign? Learning Outcomes: You will learn how to use logistic regression and decision trees to develop a direct marketing campaign, estimate the resulting profit from your campaign, and generate business insights. We will work with the datasets from Case #3.Apply the concepts and tools in the last lecture on real marketing dataBuild the best model to predict the most likely customers who will respond to a marketing campaignCalculate profits using the prediction of the modelSession 10: Introduction to Clustering and SegmentationQuestions: What is clustering? How do we use clustering to answer business questions?Learning Outcomes: Clustering is a powerful tool for dealing with unstructured data, enabling us to segment customers and develop their profiles. You will learn to recognize business applications of clustering and implement it in JMP.Learn about unstructured dataFamiliarize with different clustering techniques such as K-mean and hierarchical clusteringImplement clustering algorithms in JMPSession 11: Advanced Clustering and Introduction to Google AdwordsQuestions: What is the right number of cluster? How do we assess the validity of the clustering output? What is Google Adwords?Learning Outcomes: You will learn how to assess the validity of your clustering outputs. We will introduce the Google Adwords program, and show how clustering algorithms can be applied to segment keywords.Learn about bias-variance tradeoffsAssess the validity of the clustering outputsIntroduce the Google Adwords programQuiz # 3: Logistic Regression and Decision TreeSession 12: LAB: Online Marketing Using Google Adwords Questions: What keywords should Smart Partyware consider in its campaign?Learning Outcomes: You will apply clustering techniques to identify the most appropriate keywords, predicts their cost, and evaluate the profitability of the resulting marketing campaign. We will work with the datasets from Case #4.Build a clustering model to identify the appropriate keywords for a Google Adwords campaignPredict the cost associated with each keyword, conversion rate, and number of new customers.Calculate the expected revenue associated with your marketing campaign.Module 4: Data-Driven Operations: Optimal Allocation of Resources via Linear ProgrammingSession 13: Linear Programming (LP)Questions: How do we find the optimal solution of a linear programming using Excel Solver? What are business problems where LP techniques can be applied?Learning Outcomes: Optimization gives business a critical edge. You will learn that optimization is a powerful tool that can be applied to various business problem.. You will be able to formulate a linear program (LP) and solve LP problems using Excel Solver. Recognize linear program as a special optimization tool Understand the components of a linear programFormulate linear programs and solve it using Excel solverMake decisions utilizing optimization to allocate resources effectivelySession 14: LAB: Resource Allocation at ApplichemQuestions: Using the data on operating performance at each plant and currency exchange rate, how do we build a linear program to determine the optimal resource allocation?Learning Outcomes: You will practice how to formulate an LP for resource allocation that takes into account exchange rate between countries. We will work with the datasets from Case #5.Use data on transportation costs and operating performance to formulate an LPSolve the LP to hedge currency riskCalculate the expected production quantity at each plant and determine the shipment to each market that minimizes the total costQuiz #4: Clustering ................
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