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Data Science for Marketing Analytics Online Course

About the Course

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments.

The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.

By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions.

Course Curriculum

Data Preparation and Cleaning

  • Course Overview
  • Lesson Overview
  • Data Models and Structured Data
  • Pandas
  • Data Manipulation
  • Summary

Data Exploration and Visualization

  • Lesson Overview
  • Identifying the Right Attributes
  • Generating Targeted Insights
  • Visualizing Data
  • Summary

Unsupervised Learning: Customer Segmentation

  • Lesson Overview
  • Customer Segmentation Methods
  • Similarity and Data Standardization
  • k-means Clustering
  • Summary

Choosing the Best Segmentation Approach

  • Lesson Overview
  • Choosing the Number of Clusters
  • Different Methods of Clustering
  • Evaluation Clustering
  • Summary

Predicting Customer Revenue Using Linear Regression

  • Lesson Overview
  • Feature Engineering for Regression
  • Performing and Interpreting Linear Regression
  • Summary

Other Regression Techniques and Tools for Evaluation

  • Lesson Overview
  • Evaluating the Accuracy of a Regression Model
  • Using Regularization for Feature Selection
  • Tree Based Regression Models
  • Summary

Supervised Learning - Predicting Customer Churn

  • Lesson Overview
  • Understanding Logistic Regression
  • Creating a Data Science Pipeline
  • Modeling the Data
  • Summary

Fine-Tuning Classification Algorithms

  • Lesson Overview
  • Support Vector Machines
  • Decision Trees and Random Forests
  • Pre-processing Data and Model Evaluation
  • Performance Metrics
  • Summary

Modeling Customer Choice

  • Lesson Overview
  • Understanding Multiclass Classification
  • Class Imbalanced Data
  • Summary

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