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.
Marketing Analytics Capstone Project
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Marketing Analytics Capstone Project
This course is part of Foundations of Marketing Analytics Specialization
Instructor: David Schweidel
3,318 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(29 reviews)
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Details to know
Shareable certificate
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Assessments
4 assignments
Taught in English
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This course is part of the Foundations of Marketing Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
This module will define the goals and activities for the marketing analytics capstone project.
What's included
2 readings1 discussion prompt
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.
What's included
9 videos2 readings1 peer review1 discussion prompt
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.
What's included
3 readings1 assignment2 discussion prompts
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.
What's included
1 reading1 assignment1 peer review
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.
What's included
2 readings2 assignments1 peer review2 discussion prompts
This module provides a final congratulatory video from Professor David Schweidel.
What's included
1 video
Instructor
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