Course Outline

Module 1: Introduction to Customer Analytics

• What is customer Analytics?
• Understanding the importance of customer analytics
• Understanding how customer analytics work
• Application of customer Analysis
• Understanding how customer analytics work
• Implementation of customer analytics in a organization
• Customer Analytics Platforms
• Understanding Big Data, New Data and Better Science

Module 2: Introduction to Machine Learning

• Applications of Machine Learning
• Supervised vs Unsupervised Learning
• Libraries suitable for Machine Learning
• Linear Regression
• Logistic Regression

Module 3: Introduction to Neural Networks

• Quick recap of Neural Networks
• Activation Functions, hidden layers, hidden units
• Illustrate & Training a Perceptron
• Important Parameters of Perceptron
• Understand limitations of A Single Layer Perceptron
• Illustrate Multi-Layer Perceptron
• Back-propagation – Learning Algorithm
• Understand Back-propagation

Module 4: Supervised Learning

• K-Nearest Neighbour
• Decision Trees
• Logistic Regression
• Model Evaluation

Module 5: Customer Value Analysis

• Working with the CLV Model
• Work with the RFM Model
• The NBD-Pareto Model

Module 6: Business Experiment

• Work with the Random Forest Model
• Understand various metadata
• Metric visualization
• Understand Experiment Tracking using MLFlow
• Understand Experiment Organization

Model 7: Predictive Analytics

• Work with Regression and Time Series Models
• Learn how to train and test a dataset
• Dataset Validation
• K-Nearest Neighbor
• K-mean Clustering

Model 8: Pricing Analytics

• Pricing techniques
• Net present value capital Budgeting model
• ML for evaluating price
• Working with Price Elasticity and Optimal Pricing
• Demand curve

Model 9: Story telling through Analytics

• Understand the need to tell a story
• How to create one in ML
• Understand different data types to deal with Understand different charts to use for storytelling
• Data Exploration and Visualization
• Model comparison

Module 10: Decision Making

• Dealing with risks and analyzing them
• Risk optimization
• Work with the ROI model
• Work with the Flow charts
• Create Decision trees
• Work with the Random Forests for decision making