English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 46 Lessons (8h 52m) | 1.82 GB

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 Descriptions:

- Lesson 1: Introduction to Predictive Analytics
- Lesson 2: Introduction to Predictive Analytics and Data Mining
- Lesson 3: The Data Mining Process
- Lesson 4: Data and Methods in Data Mining
- Lesson 5: Data Mining Algorithms
- Lesson 6: Text Analytics and Text Mining
- Lesson 7: Big Data Analytics
- Lesson 8: Predictive Analytics Best Practices

Skill Level:

There is not a required minimum skill or knowledge level to take this course. Because of its holistic coverage, the course appeals to anyone (students and professionals) at any level of technical or managerial skill levels who is interested in learning about predictive analytics and its value propositions.

Learn How To:

The course provides thorough yet easy-to-digest coverage of predictive analytics concepts, theories, and best practices, followed by visual, intuitive, and highly practical hands-on illustrative examples using a variety of data sets and industry-leading software tools and platforms.

Who Should Take This Course:

This course is designed for anyone who is interested in learning about the best practices of predictive analytics and anyone who seeks to rapidly move into practical application of this popular technology with a minimal investment of time and resources.

Course Requirements:

There are no specific prerequisites or must-have requirements for this course. It is designed to attract and benefit anyone at any skill and managerial level who is interested in learning predictive analytics.

## Table of Contents

**Introduction**

1 Predictive Analytics Introduction

**Lesson 1 Introduction to Predictive Analytics**

2 Topics

3 What Is Analytics and Where Does Data Mining Fit In

4 Popularity and Application Areas of Analytics

5 An Analytics Timeline and a Simple Taxonomy

6 Cutting Edge of Analytics IBM Watson

7 Real-world Analytics Applications

**Lesson 2 Introduction to Predictive Analytics and Data Mining**

8 Topics

9 What Is Data Mining, and What Is It Not

10 The Most Common Data Mining Applications and Tools

11 Demonstration of Predictive Modeling with Python

12 Demonstration of Predictive Modeling with KNIME

**Lesson 3 The Data Mining Process**

13 Topics

14 The Knowledge Discovery in Databases (KDD) Process

15 Cross-Industry Standard Process for Data Mining (CRISP-DM)

16 Sample, Explore, Modify, Model, and Assess (SEMMA) Process and Six Sigma Process

17 Demonstration of Data Mining Tools IBM SPSS Modeler and R

**Lesson 4 Data and Methods in Data Mining**

18 Topics

19 The Nature of Data in Data Mining

20 Data Mining Methods Predictive versus Descriptive

21 Evaluation Methods in Data Mining

22 Classification with Decision Trees

23 Clustering with the k-means Algorithm

24 Association Analysis with the Apriori Algorithm

**Lesson 5 Data Mining Algorithms**

25 Topics

26 Nearest Neighbor Algorithm for Prediction Modeling

27 Artificial Neural Networks (ANN) and Support Vector Machines (SVM)

28 Linear Regression and Logistic Regression

29 Demonstration of Linear Regression with Python and KNIME

**Lesson 6 Text Analytics and Text Mining**

30 Topics

31 Introduction to Text Mining and Natural Language Processing

32 Text Mining Applications and Text Mining Process

33 Text Mining Tools and Demonstration of Text Mining Using Rapid Miner

34 Text Mining Tools and Demonstration of Sentiment Analysis and Topic Modeling with KNIME

**Lesson 7 Big Data Analytics**

35 Topics

36 What Is Big Data and Where Does It Come From

37 Fundamental Concepts and Technologies of Big Data

38 Who Are Data Scientists and Where Do They Come From

39 Demonstration of Big Data Analytics (SAS Visual Analytics)

**Lesson 8 Predictive Analytics Best Practices**

40 Topics

41 Defining Model Ensembles and Their Pros and Cons

42 Bias-Variance Tradeoff in Predictive Analytics

43 Treating the Data-Imbalance Problem with Over- and Undersampling

44 Explainable MLAIPredictive Analytics

45 Showcasing Better Practices with a Comprehensive Model of Customer Churn Analysis

**Summary**

46 Predictive Analytics Summary

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