Course Description

Silhouette of a hand at sunset holding a generated image of a ball of connected lights representing a data network


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 Outline

  • Lesson 1: Data Preparation and Cleaning
    • Data Models and Structured Data
    • pandas
    • Data Manipulation
  • Lesson 2: Data Exploration and Visualization
    • Identifying the Right Attributes
    • Generating Targeted Insights
    • Visualizing Data
  • Lesson 3: Unsupervised Learning: Customer Segmentation
    • Customer Segmentation Methods
    • Similarity and Data Standardization
    • k-means Clustering
  • Lesson 4: Choosing the Best Segmentation Approach
    • Choosing the Number of Clusters
    • Different Methods of Clustering
    • Evaluating Clustering
  • Lesson 5: Predicting Customer Revenue Using Linear Regression
    • Understanding Regression
    • Feature Engineering for Regression
    • Performing and Interpreting Linear Regression
  • Lesson 6: Other Regression Techniques and Tools for Evaluation
    • Evaluating the Accuracy of a Regression Model
    • Using Regularization for Feature Selection
    • Tree-Based Regression Models
  • Lesson 7: Supervised Learning: Predicting
    • Customer Churn
    • Classification Problems
    • Understanding Logistic Regression
    • Creating a Data Science Pipeline
  • Lesson 8: Fine-Tuning Classification Algorithms
    • Support Vector Machine
    • Decision Trees
    • Random Forest
    • Preprocessing Data for Machine Learning Models
    • Model Evaluation
    • Performance Metrics
  • Lesson 9: Modeling Customer Choice
    • Understanding Multiclass Classification
    • Class Imbalanced Data

Learner Outcomes

At the end of this program, you will be able to:

  • Analyze and visualize data in Python using pandas and Matplotlib
  • Study clustering techniques, such as hierarchical and k-means clustering
  • Create customer segments based on manipulated data
  • Predict customer lifetime value using linear regression
  • Use classification algorithms to understand customer choice
  • Optimize classification algorithms to extract maximal information


Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics
efforts. It’ll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or
Tableau is useful but not necessary.
Thank you for your interest in this course. Unfortunately, the course you have selected is currently not open for enrollment. Please complete a Course Inquiry so that we may promptly notify you when enrollment opens.
Required fields are indicated by .