Customer Analytics in Python 2023

Udemy
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Customer Analytics in Python 2023 Udemy Coupon

Customer Analytics in Python 2023

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

Created by 365 Careers, 365 Iliya Valchanov | 5 hours on-demand video course

Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. Welcome to Customer Analytics in Python – the place where marketing and data science meet! This course is the best way to distinguish yourself with a very rare and extremely valuable skillset.

This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python. Since Customer Analytics is a broad topic, we have created 5 different parts to explore various sides of the analytical process. Each of them will have their strong sides and shortcomings. We will explore both sides of the coin for each part, while making sure to provide you with nothing but the most important and relevant information!

What you’ll 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

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