Data Science for Marketing Analytics Online Course

Data Science for Marketing Analytics Online Course

About the Course

Data Science for Marketing Analytics guides you through the complete data analytics lifecycle—from processing raw data to segmenting customer groups and building predictive models tailored to those segments.

The course begins with an introduction to essential Python libraries like pandas and Matplotlib, teaching you how to read, manipulate, and visualize both categorical and continuous data. You’ll then move on to customer segmentation, learning how to apply various clustering techniques and evaluate their effectiveness.

As the course progresses, you’ll dive into model selection, develop a linear regression model to predict customer lifetime value, and explore advanced regression techniques and model evaluation tools. You'll also gain hands-on experience with classification algorithms to forecast customer behavior, ultimately building a churn prediction model for product choice analysis.

By the end of this course, you will be able to:

• Perform end-to-end marketing data analysis
• Apply clustering and predictive modeling techniques
• Predict customer value and behavior
• Build interactive dashboards and marketing reports using Python
• This course equips you with practical skills to turn data into actionable insights, helping you make informed marketing decisions.

Who should take this Course?

The Data Science for Marketing Analytics Online Course is ideal for marketing professionals, data analysts, business strategists, and students who want to leverage data science techniques to drive marketing decisions. It’s also suitable for professionals aiming to optimize campaigns, understand customer behavior, and measure ROI using tools like Python, Excel, and machine learning models. A basic understanding of marketing principles and data analysis or familiarity with Excel or Python is recommended for a successful learning experience.

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