Lessons 1: Introduction

Lessons 2: Introduction to Analytics

• What’s in a Name?
• Why the Sudden Popularity of Analytics and Data Science?
• The Application Areas of Analytics
• The Main Challenges of Analytics
• A Longitudinal View of Analytics
• A Simple Taxonomy for Analytics
• The Cutting Edge of Analytics: IBM Watson

Lessons 3: Predictive Analytics and Data Mining

• What Is Data Mining?
• What Data Mining Is Not
• The Most Common Data Mining Applications
• What Kinds of Patterns Can Data Mining Discover
• Popular Data Mining Tools
• The Dark Side of Data Mining: Privacy Concerns

Lessons 4: Standardised Processes for Predictive Analytics

• The Knowledge Discovery in Databases (KDD) Process
• Cross-Industry Standard Process for Data Mining (CRISP-DM)
• SEMMA
• SEMMA Versus CRISP-DM
• Six Sigma for Data Mining
• Which Methodology Is Best?

Lessons 5: Data and Methods for Predictive Analytics

• The Nature of Data in Data Analytics
• Pre-processing of Data for Analytics
• Data Mining Methods
• Prediction
• Classification
• Decision Trees
• Cluster Analysis for Data Mining
• k-Means Clustering Algorithm
• Apriori Algorithm
• Data Mining and Predictive Analytics Misconceptions and Realities

Lessons 6: Algorithms for Predictive Analytics

• Naive Bayes
• Nearest Neighbour
• Similarity Measure: The Distance Metric
• Artificial Neural Networks
• Support Vector Machines
• Linear Regression
• Logistic Regression
• Time-Series Forecasting

Lessons 7: Advanced Topics in Predictive Modelling

• Model Ensembles
• Bias–Variance Trade-off in Predictive Analytics
• Imbalanced Data Problems in Predictive Analytics
• Explainability of Machine Learning Models for Predictive Analytics

Lessons 8: Text Analytics, Topic Modeling and Sentiment Analysis

• Natural Language Processing
• Text Mining Applications
• The Text Mining Process
• Text Mining Tools
• Topic Modeling
• Sentiment Analysis

Lessons 9: Big Data for Predictive Analytics

• Where Does Big Data Come From?
• The Vs That Define Big Data
• Fundamental Concepts of Big Data
• The Business Problems That Big Data Analytics Addresses
• Big Data Technologies
• Data Scientists
• Big Data and Stream Analytics
• Data Stream Mining

Lessons 10: Deep Learning and Cognitive Computing

• Introduction to Deep Learning
• Basics of “Shallow” Neural Networks
• Elements of an Artificial Neural Network
• Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Networks and Long Short-Term Memory Networks
• Computer Frameworks for Implementation of Deep Learning
• Cognitive Computing

Appendix A: KNIME and the Landscape of Tools for Business Analytics and Data Science

• Project Constraints: Time and Money
• The Learning Curve
• The KNIME Community
• Correctness and Flexibility
• Extensive Coverage of Data Science Techniques
• Data Science in the Enterprise

Appendix B: Videos

• Introduction to Predictive Analytics
• Introduction to Predictive Analytics and Data Mining
• The Data Mining Process
• Data and Methods in Data Mining
• Data Mining Algorithms
• Text Analytics and Text Mining
• Big Data Analytics

Introduction to Predictive Analytics and Data Mining

• Creating a Decision Tree in Python
• Creating a Decision Tree in KNIME

Data and Methods for Predictive Analytics

• Running k-Means Clustering Algorithm in KNIME

Algorithms for Predictive Analytics

• Using the k-Nearest Neighbour Algorithm
• Using ANN and SVM for Prediction Type Analytics Problems
• Implementing Linear Regression in Python
• Implementing Linear Regression Model in KNIME

Advanced Topics in Predictive Modeling

• Showcasing Better Practices With a Customer Churn Analysis

Text Analytics, Topic Modeling, and Sentiment Analysis

• Performing Topic Modeling
• Performing Sentiment Analysis