Predictive Analytics, 2nd Edition

سرفصل های دوره

Introduction

001. Predictive Analytics Introduction

Lesson 1 Introduction to Predictive Analytics

001. Topics

002. 1.1 What Is Analytics and Where Does Data Mining Fit In

003. 1.2 Popularity and Application Areas of Analytics

004. 1.3 An Analytics Timeline and a Simple Taxonomy

005. 1.4 Cutting Edge of Analytics IBM Watson

006. 1.5 Real-world Analytics Applications

Lesson 2 Introduction to Predictive Analytics and Data Mining

001. Topics

002. 2.1 What Is Data Mining, and What Is It Not

003. 2.2 The Most Common Data Mining Applications and Tools

004. 2.3 Demonstration of Predictive Modeling with Python

005. 2.4 Demonstration of Predictive Modeling with KNIME

Lesson 3 The Data Mining Process

001. Topics

002. 3.1 The Knowledge Discovery in Databases (KDD) Process

003. 3.2 Cross-Industry Standard Process for Data Mining (CRISP-DM)

004. 3.3 Sample, Explore, Modify, Model, and Assess (SEMMA) Process and Six Sigma Process

005. 3.4 Demonstration of Data Mining Tools IBM SPSS Modeler and R

Lesson 4 Data and Methods in Data Mining

001. Topics

002. 4.1 The Nature of Data in Data Mining

003. 4.2 Data Mining Methods Predictive versus Descriptive

004. 4.3 Evaluation Methods in Data Mining

005. 4.4 Classification with Decision Trees

006. 4.5 Clustering with the k-means Algorithm

007. 4.6 Association Analysis with the Apriori Algorithm

Lesson 5 Data Mining Algorithms

001. Topics

002. 5.1 Nearest Neighbor Algorithm for Prediction Modeling

003. 5.2 Artificial Neural Networks (ANN) and Support Vector Machines (SVM)

004. 5.3 Linear Regression and Logistic Regression

005. 5.4 Demonstration of Linear Regression with Python and KNIME

Lesson 6 Text Analytics and Text Mining

001. Topics

002. 6.1 Introduction to Text Mining and Natural Language Processing

003. 6.2 Text Mining Applications and Text Mining Process

004. 6.3 Text Mining Tools and Demonstration of Text Mining Using Rapid Miner

005. 6.4 Text Mining Tools and Demonstration of Sentiment Analysis and Topic Modeling with KNIME

Lesson 7 Big Data Analytics

001. Topics

002. 7.1 What Is Big Data and Where Does It Come From

003. 7.2 Fundamental Concepts and Technologies of Big Data

004. 7.3 Who Are Data Scientists and Where Do They Come From

005. 7.4 Demonstration of Big Data Analytics (SAS Visual Analytics)

Lesson 8 Predictive Analytics Best Practices

001. Topics

002. 8.1 Defining Model Ensembles and Their Pros and Cons

003. 8.2 Bias-Variance Tradeoff in Predictive Analytics

004. 8.3 Treating the Data-Imbalance Problem with Over- and Undersampling

005. 8.4 Explainable MLAIPredictive Analytics

006. 8.5 Showcasing Better Practices with a Comprehensive Model of Customer Churn Analysis

Summary

001. Predictive Analytics Summary

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