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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

3,575 already enrolled

Included with

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6 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 6 modules in 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.

Module details

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

9 videosTotal 91 minutes

Meaningful Marketing Insights - Course Objectives & Example 1: Political Advertising12 minutes
Meaningful Marketing Insights - Course Goals & Example 2: Performing Arts Centers9 minutes
Meaningful Marketing Insights - Organizing Data11 minutes
Meaningful Marketing Insights - The Motion Picture Industry12 minutes
Meaningful Markting Insights - Excel Analysis of Motion Picture Industry Data13 minutes
Meaningful Marketing Insights - Displaying Conditional Distributions5 minutes
Meaningful Marketing Insights - Analyzing Qualitative Variables4 minutes
Meaningful Marketing Insights - Steps in Constructing Histograms11 minutes
Meaningful Marketing Insights - Common Descriptive Statistics for Quantitative Data15 minutes

2 readingsTotal 85 minutes

Activity & Explanation of Review Content45 minutes
Meaningful Marketing Insights - Parts 2 - 340 minutes

1 peer reviewTotal 60 minutes

"On-time" Loan Status versus "Risky" Loan Status60 minutes

1 discussion promptTotal 15 minutes

Classifying Individuals15 minutes

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.