About this course: This capstone project will give you an opportunity to apply what we have covered in the Foundations of Marketing Analytics specialization. By the end of this capstone project, you will have conducted exploratory data analysis, examined pairwise relationships among different variables, and developed and tested a predictive model to solve a marketing analytics problem. It is highly recommended that you complete all courses within the Foundations of Marketing Analytics specialization before starting the capstone course.
Who is this class for: This course is designed for learners seeking knowledge about marketing analytics. It is recommended that the learner have knowledge of Microsoft Excel.
Created by: Emory University
Taught by: David Schweidel, Associate Professor of Marketing
Goizueta Business School
| Basic Info | Course 6 of 6 in the Foundations of Marketing Analytics Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Marketing Analytics Project Description
This module will define the goals and activities for the marketing analytics capstone project.
2 readings
- Reading: Capstone Overview
- Reading: Pre-Readings
- Discussion Prompt: Pre-exercise Discussion
WEEK 2
Exploratory Analysis
In this module, we will begin to examine individual variables and their relationship to the status of the loan. Note, this module includes review items from previous courses in the specialization. This content is not required, but recommended as content to revisit.
9 videos, 2 readings
- Reading: Activity & Explanation of Review Content
- Discussion Prompt: Classifying Individuals
- Reading: Meaningful Marketing Insights - Parts 2 - 3
- Video: Meaningful Marketing Insights - Course Goals & Example 2: Performing Arts Centers
- Video: Meaningful Marketing Insights - Organizing Data
- Video: Meaningful Marketing Insights - The Motion Picture Industry
- Video: Meaningful Markting Insights - Excel Analysis of Motion Picture Industry Data
- Video: Meaningful Marketing Insights - Displaying Conditional Distributions
- Video: Meaningful Marketing Insights - Analyzing Qualitative Variables
- Video: Meaningful Marketing Insights - Steps in Constructing Histograms
- Video: Meaningful Marketing Insights - Common Descriptive Statistics for Quantitative Data
Graded: "On-time" Loan Status versus "Risky" Loan Status
WEEK 3
Data Preparation and Model Building
While there are many ways to build a classification model, we will focus on using logistic regression, a common tool for marketing problems in which the dependent variable is binary. We will begin by choosing a single predictor variable and then determine which other variables need to be added to our analysis. In this module, we will focus on developing alternative models that all have a single predictor.
3 readings, 1 practice quiz
- Reading: Data Preparation Instructions
- Discussion Prompt: Recoding the Variables
- Reading: Populating the Template
- Practice Quiz: Logistic Regression Practice
- Discussion Prompt: Analysis Review
- Reading: Review Forecasting Models for Marketing Decisions, Parts 1 - 3
WEEK 4
Model Validation and Comparison
In the previous module, we estimated a model linking home ownership to whether or not a loan is considered risky. In this module, we will begin by assessing the accuracy of this model relative to a naïve model. We will then use this spreadsheet as a means of assessing how well the model performs when different predictors are used.
1 reading, 1 practice quiz
- Reading: Model Validation
- Practice Quiz: Model Validation
Graded: Logistic Regression
WEEK 5
Incorporating Multiple Predictor Variables
In this module, we will generalize the logistic regression tool that was developed to include multiple predictor variables. We will also consider an alternative means of evaluating the performance of the model.
2 readings, 2 practice quizzes
- Reading: Incorporating Additional Predictors
- Practice Quiz: Predictors
- Discussion Prompt: Predictors (Challenges and Questions)
- Reading: An Alternative Means of Evaluating Performance
- Practice Quiz: Logistic Regression
- Discussion Prompt: ROC Curve
Graded: Evaluating Combinations of Predictor Variables
WEEK 6
Congratulations!
This module provides a final congratulatory video from Professor David Schweidel.
1 video
FAQs
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Emory University
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| Access to course materials | Available |
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