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Updated in [July 18th, 2023]

This course, Customer Analytics in Python, is the perfect way to distinguish yourself with a rare and extremely valuable skillset. It covers some of the most exciting methods used by companies, all implemented in Python. The course is divided into 5 major parts, each of which will have its strong sides and shortcomings. The course author, Nikolay Georgiev, is a Ph.D. who has focused on marketing analytics during his academic career and has gained significant practical experience while working as a consultant on numerous world-class projects.

In the first part, students will be introduced to the relevant theory needed to start performing customer analytics. The second part will cover cluster analysis and dimensionality reduction to help students segment their customers. The third part consists of applying descriptive statistics as the exploratory part of the analysis. The fourth part will engage with elasticity modeling for purchase probability, brand choice, and purchase quantity. Finally, the fifth part will leverage the power of Deep Learning to predict future behavior.

Throughout the course, students will be working with several popular packages such as NumPy, SciPy, and scikit-learn. They will also be able to visualize the data appropriately to build their understanding of the methods even further.

This course is created by 3 instructors working closely together to provide the most beneficial learning experience. With 550,000+ students here on Udemy, the best education requires a remarkable teaching collective and a practical approach. This course provides both.

Course Syllabus

Introduction

A Brief Marketing Introduction

Setting up the Environment

Segmentation Data

Hierarchical Clustering

K-Means Clustering

K-Means Clustering based on Principal Component Analysis

Purchase Data

Descriptive Analyses by Segments

Modeling Purchase Incidence

Modeling Brand Choice

Modeling Purchase Quantity

Deep Learning for Conversion Prediction