Predictive Analytics 2e

Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, machine learning, and data mining. He is often invited to national and international conferences to deliver keynote presentations on topics related to data/text mining, business intelligence, decision support systems, business analytics, data science, and knowledge management. Prior to his appointment as a professor at Oklahoma State University in 2001, Dr. Delen worked in industry for more than 15 years, developing and delivering business analytics solutions to companies. His most recent industrial work was at a privately owned applied research and consulting company, Knowledge Based Systems, Inc. (kbsi.com), in College Station, Texas, as a research scientist. During his five years at KBSI, Dr. Delen led a number of projects related to decision support systems, enterprise engineering, information systems development, and advanced business analytics that were funded by private industry and federal agencies, including several branches of the Department of Defense, NASA, National Science Foundation, National Institute for Standards and Technology, and the Department of Energy. Today, in addition to his academic endeavors, Dr. Delen provides professional education, mentoring, and consulting services to businesses in assessing their analytics, data science and information system needs, and helping them in developing state-of-the-art computerized decision support systems. He has published over 180 peer-reviewed research articles in major journals. He has also authored eleven books and textbooks in the broad area of business analytics, data science, and business intelligence. Dr. Delen regularly chairs tracks and mini-tracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief of the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards including the prestigious Fulbright scholar, Regents’ Distinguished Teacher and Researcher, President’s Outstanding Researcher, and big data mentor awards.

Introduction

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

Summary