Customer Analytics in Python

Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks

Created by: 365 Careers
Creating opportunities for Data Science and Finance students

Created by: 365 Iliya Valchanov
Instructor at Udemy

What Will I Learn?

• Master beginner and advanced customer analytics
• Learn the most important type of analysis applied by mid and large companies
• Gain access to a professional team of trainers with exceptional quant skills
• Wow interviewers by acquiring a highly desired skill
• Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity;
• Apply segmentation on your customers, starting from raw data and reaching final customer segments;
• Perform K-means clustering with a customer analytics focus;
• Apply Principal Components Analysis (PCA) on your data to preprocess your features;
• Combine PCA and K-means for even more professional customer segmentation;
• Deploy your models on a different dataset;
• Learn how to model purchase incidence through probability of purchase elasticity;
• Model brand choice by exploring own-price and cross-price elasticity;
• Complete the purchasing cycle by predicting purchase quantity elasticity
• Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy
• Be able to optimize your neural networks to enhance results

Requirements

• You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
• Basic Python programming
• A willingness and enthusiasm to learn and practice

Target audience

• People who want a career in Data Science
• People who want a career in Business Intelligence
• Individuals who are passionate about numbers and quant analysis
• People working in Data Science looking to expand their knowledge into Marketing analytics
• People working in Marketing, looking for career growth in the realms of Data Science