À propos de ce cours : 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.
Les destinataires de ce cours : This course is designed for learners seeking knowledge about marketing analytics. It is recommended that the learner have knowledge of Microsoft Excel.
Créé par : Emory University
Enseigné par : David Schweidel, Associate Professor of Marketing
Goizueta Business School
| Informations de base | Cours 6 de 6 dans Foundations of Marketing Analytics Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Marketing Analytics Project Description
This module will define the goals and activities for the marketing analytics capstone project.
2 lectures
- Материал для самостоятельного изучения: Capstone Overview
- Материал для самостоятельного изучения: Pre-Readings
- Вопрос для обсуждения: Pre-exercise Discussion
SEMAINE 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 vidéos, 2 lectures
- Материал для самостоятельного изучения: Activity & Explanation of Review Content
- Вопрос для обсуждения: Classifying Individuals
- Материал для самостоятельного изучения: Meaningful Marketing Insights - Parts 2 - 3
- Vidéo: Meaningful Marketing Insights - Course Goals & Example 2: Performing Arts Centers
- Vidéo: Meaningful Marketing Insights - Organizing Data
- Vidéo: Meaningful Marketing Insights - The Motion Picture Industry
- Vidéo: Meaningful Markting Insights - Excel Analysis of Motion Picture Industry Data
- Vidéo: Meaningful Marketing Insights - Displaying Conditional Distributions
- Vidéo: Meaningful Marketing Insights - Analyzing Qualitative Variables
- Vidéo: Meaningful Marketing Insights - Steps in Constructing Histograms
- Vidéo: Meaningful Marketing Insights - Common Descriptive Statistics for Quantitative Data
Noté: "On-time" Loan Status versus "Risky" Loan Status
SEMAINE 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 lectures, 1 quiz pour s'exercer
- Материал для самостоятельного изучения: Data Preparation Instructions
- Вопрос для обсуждения: Recoding the Variables
- Материал для самостоятельного изучения: Populating the Template
- Тренировочный тест: Logistic Regression Practice
- Вопрос для обсуждения: Analysis Review
- Материал для самостоятельного изучения: Review Forecasting Models for Marketing Decisions, Parts 1 - 3
SEMAINE 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 lecture, 1 quiz pour s'exercer
- Материал для самостоятельного изучения: Model Validation
- Тренировочный тест: Model Validation
Noté: Logistic Regression
SEMAINE 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 lectures, 2 quiz pour s'exercer
- Материал для самостоятельного изучения: Incorporating Additional Predictors
- Тренировочный тест: Predictors
- Вопрос для обсуждения: Predictors (Challenges and Questions)
- Материал для самостоятельного изучения: An Alternative Means of Evaluating Performance
- Тренировочный тест: Logistic Regression
- Вопрос для обсуждения: ROC Curve
Noté: Evaluating Combinations of Predictor Variables
SEMAINE 6
Congratulations!
This module provides a final congratulatory video from Professor David Schweidel.
1 vidéo
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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