Data Science for Marketing Analytics

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 course, 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 sections, 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.

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Learning Objectives

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


Course Details

Course Outline

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

    Who is it For?

    Target Audience

    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.

    Data Science for Marketing Analytics

    Course Length : 3 Days (24 Hours)

    There are currently no scheduled dates for this course. Please contact us for more information.

    Need Help Picking the Right Course? Give us a call! 901-375-1533