6+ Hours of Video Instruction
This course is designed to bring a holistic, approachable, and best-practice-driven learning experience to predictive analytics.
Updated and revamped, Predictive Analytics, 2nd Edition, provides comprehensive (yet easy-to-digest) coverage of business analytics concepts, applications, methods, and tools, with a special emphasis on predictive modeling and analysis. Over the course of the eight lessons, you will learn fundamental concepts, methods, and algorithms of business analytics and data mining, as well as their application areas and best practices. You also learn how to use a variety of software tools (both commercial as well as free/open source) and how to use those tools to discover knowledge from a wide variety of data sources.
At the end of the course, you will not only know what predictive analytics is and what it can do for an organization but also develop basic skills to practice predictive analytics using numerous tools and platforms, most of which are free and open source. The course is designed to provide thorough coverage of the underlying concepts and definitions of predictive analytics in order to demystify the concepts and terminology of these popular evidence-based managerial decisioning trends and then help build hands-on skills with the most popular analytics tools and platforms using intuitive examples and data sets.
Lesson 1: Introduction to Predictive Analytics
1.1 What Is Analytics and Where Does Data Mining Fit In?
1.2 Popularity and Application Areas of Analytics
1.3 An Analytics Timeline and a Simple Taxonomy
1.4 Cutting Edge of Analytics: IBM Watson
1.5 Real-World Analytics Applications
Lesson 2: Introduction to Predictive Analytics and Data Mining
2.1 What Is Data Mining, and What Is It Not?
2.2 The Most Common Data Mining Applications and Tools
2.3 Demonstration of Predictive Modeling with Python
2.4 Demonstration of Predictive Modeling with KNIME
Lesson 3: The Data Mining Process
3.1 The Knowledge Discovery in Databases (KDD) Process
3.2 Cross-Industry Standard Process for Data Mining (CRISP-DM)
3.3 Sample, Explore, Modify, Model and Assess (SEMMA) Process and Six Sigma Process
3.4 Demonstration of Data Mining Tools: IBM SPSS Modeler and R
Lesson 4: Data and Methods in Data Mining
4.1 The Nature of Data in Data Mining
4.2 Data Mining Methods: Predictive versus Descriptive
4.3 Evaluations Methods in Data Mining
4.4 Classification with Decision Trees
4.5 Clustering with the k-Means Algorithm
4.6 Association Analysis with Apriori Algorithm
Lesson 5: Data Mining Algorithms
5.1 The Nearest Neighbor Algorithm for Prediction Modeling
5.2 Artificial Neural Networks (ANN) and Support Vector Machines (SVM)
5.3 Linear Regression and Logistic Regression
5.4 Demonstration of Linear Regression and Logistic Regression with Python and KNIME
Lesson 6: Text Analytics and Text Mining
6.1 Introduction to Text Mining and Natural Language Processing
6.2 Text Mining Applications and Text Mining Process
6.3 Text Mining Tools and Demonstration of Text Mining Using Rapid Miner
6.4 Text Mining Tools and Demonstration of Sentiment Analysis and Topic Modeling with KNIME
Lesson 7: Big Data Analytics
7.1 What Is Big Data and Where Does It Come From?
7.2 Fundamental Concepts and Technologies of Big Data
7.3 Who Are Data Scientists and Where Do They Come From?
7.4 Demonstration of Big Data Analytics (SAS Visual Analytics)
Lesson 8: Predictive Analytics Best Practices
8.1 Defining and Model Ensembles and Their Pros and Cons
8.2 Bias-Variance Tradeoff in Predictive Analytics
8.3 Treating Data-Imbalance Problem with Over- and Undersampling
8.4 Explainable ML/AI/Predictive Analytics
8.5 Showcasing Better Practices with a Comprehensive Model of Customer Churn Analysis