Marketing Analytics Capstone Project

Methode

Marketing Analytics Capstone Project

Coursera (CC)
Logo von Coursera (CC)
Bewertung: starstarstarstar_halfstar_border 7,2 Bildungsangebote von Coursera (CC) haben eine durchschnittliche Bewertung von 7,2 (aus 6 Bewertungen)

Tipp: Haben Sie Fragen? Für weitere Details einfach auf "Kostenlose Informationen" klicken.

Beschreibung

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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 kno…

Gesamte Beschreibung lesen

Frequently asked questions

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Capstone Courses, Persönliche Effektivität, Persönlichkeitsentwicklung, Problemanalyse und -lösung und SPSS.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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. Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Emory University Emory University, located in Atlanta, Georgia, is one of the world's leading research universities. Its mission is to create, preserve, teach and apply knowledge in the service of humanity.

Syllabus


WEEK 1


Marketing Analytics Project Description
This module will define the goals and activities for the marketing analytics capstone project.


2 readings expand


  1. Reading: Capstone Overview
  2. Reading: Pre-Readings
  3. 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 expand


  1. Reading: Activity & Explanation of Review Content
  2. Discussion Prompt: Classifying Individuals
  3. Reading: Meaningful Marketing Insights - Parts 2 - 3
  4. Video: Meaningful Marketing Insights - Course Objectives & Example 1: Political Advertising
  5. Video: Meaningful Marketing Insights - Course Goals & Example 2: Performing Arts Centers
  6. Video: Meaningful Marketing Insights - Organizing Data
  7. Video: Meaningful Marketing Insights - The Motion Picture Industry
  8. Video: Meaningful Markting Insights - Excel Analysis of Motion Picture Industry Data
  9. Video: Meaningful Marketing Insights - Displaying Conditional Distributions
  10. Video: Meaningful Marketing Insights - Analyzing Qualitative Variables
  11. Video: Meaningful Marketing Insights - Steps in Constructing Histograms
  12. 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 expand


  1. Reading: Data Preparation Instructions
  2. Discussion Prompt: Recoding the Variables
  3. Reading: Populating the Template
  4. Practice Quiz: Logistic Regression Practice
  5. Discussion Prompt: Analysis Review
  6. 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 expand


  1. Reading: Model Validation
  2. 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 expand


  1. Reading: Incorporating Additional Predictors
  2. Practice Quiz: Predictors
  3. Discussion Prompt: Predictors (Challenges and Questions)
  4. Reading: An Alternative Means of Evaluating Performance
  5. Practice Quiz: Logistic Regression
  6. 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 expand


  1. Video: Congratulations

Werden Sie über neue Bewertungen benachrichtigt

Es wurden noch keine Bewertungen geschrieben.

Schreiben Sie eine Bewertung

Haben Sie Erfahrung mit diesem Kurs? Schreiben Sie jetzt eine Bewertung und helfen Sie Anderen dabei die richtige Weiterbildung zu wählen. Als Dankeschön spenden wir € 1,00 an Stiftung Edukans.

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

Bitte füllen Sie das Formular so vollständig wie möglich aus

Anrede
(optional)
(optional)
(optional)
(optional)
(optional)

Haben Sie noch Fragen?

(optional)
Damit Ihnen per E-Mail oder Telefon weitergeholfen werden kann, speichern wir Ihre Daten.
Mehr Informationen dazu finden Sie in unseren Datenschutzbestimmungen.