Download Predictive Analytics, 2nd Edition

What you would learn in Predictive Analytics, 2nd Edition course?

Predictive Analytics 2nd Edition is comprehensive (yet easy to understand) information on business analysis concepts, apps, methodologies, and instruments, emphasizing predictive analysis and modeling concentration. In 8 lessons, students will master the basic concepts, strategies, and algorithms in the field of business analytics, data mining, their areas of application, and the best methods. Learn how to utilize a variety of programs (both commercial and open source or free) and how you can use the tools to uncover information from a myriad of different data sources.
After the course, you'll be aware of the meaning of predictive analytics and what it could do for an organization. You will also learn the basics of applying predictive analytics with many platforms and tools, many of which are open and accessible. The course will give a thorough understanding of the fundamental theories and terms of predictive analytics to simplify the concepts and terminology used to describe the popular, evidence-based management decision-making patterns and help you develop practical skills using the most widely used analytical tools as well as platforms by using simple instances and data set.

Lesson Descriptions:

• Lesson 1 An Introduction to Predictive Analytics
• Lesson 2 A Brief Introduction to Predictive Analytics and Data Mining
• Lesson 3 the Data Mining Process
• Lesson 4 The use of data and methods in Data Mining
• Leçon 5: Data Mining Algorithms
• Sixth lesson 7: Analytics and Text Analytics as well as Text Mining
• Lesson 7: Big Data Analytics
• Lesson 8: Predictive Analytics Best Practices

Skills Level
There is no requirement for minimum knowledge or level for this course. Due to its broad content, it is an excellent course for everyone (students or experts) with managerial or technical skills who would like to learn more about the predictive capabilities of analytics and its benefits.

Learn How to:
The course is comprehensive and straightforward information on the theory, concepts, and best practices of predictive analytics concepts. This is then followed by visual, logical, and efficient examples that are illustrative and hands-on using various data sets and industry-leading technology platforms and tools.

Who Should Be Taking the Course?
This course is intended to help anyone looking to learn more about the best practices for predictive analytics and for anyone looking to quickly advance into applying this technology, with minimal investment in resources and time.

The Course requirements are:
There aren't any specific requirements or prerequisites to take this class. It's designed to draw and assist anyone with any level of skill or managerial interest in predictive analytics.

Course Content:

• Introduction
  1. Predictive Analytics: Introduction
• Lesson 1: Introduction to Predictive Analytics
  1. Topics
  2. 1.1 What Is Analytics and Where Does Data Mining Fit In?
  3. 1.2 Popularity and Application Areas of Analytics
  4. 1.3 An Analytics Timeline and a Simple Taxonomy
  5. 1.4 Cutting Edge of Analytics: IBM Watson
  6. 1.5 Real-world Analytics Applications
• Lesson 2: Introduction to Predictive Analytics and Data Mining
  1. Topics
  2. 2.1 What Is Data Mining, and What Is It Not?
  3. 2.2 The Most Common Data Mining Applications and Tools
  4. 2.3 Demonstration of Predictive Modeling with Python
  5. 2.4 Demonstration of Predictive Modeling with KNIME
• Lesson 3: The Data Mining Process
  1. Topics
  2. 3.1 The Knowledge Discovery in Databases (KDD) Process
  3. 3.2 Cross-Industry Standard Process for Data Mining (CRISP-DM)
  4. 3.3 Sample, Explore, Modify, Model, and Assess (SEMMA) Process and Six Sigma Process
  5. 3.4 Demonstration of Data Mining Tools: IBM SPSS Modeler and R
• Lesson 4: Data and Methods in Data Mining
  1. Topics
  2. 4.1 The Nature of Data in Data Mining
  3. 4.2 Data Mining Methods: Predictive versus Descriptive
  4. 4.3 Evaluation Methods in Data Mining
  5. 4.4 Classification with Decision Trees
  6. 4.5 Clustering with the k-means Algorithm
  7. 4.6 Association Analysis with the Apriori Algorithm
• Lesson 5: Data Mining Algorithms
  1. Topics
  2. 5.1 Nearest Neighbor Algorithm for Prediction Modeling
  3. 5.2 Artificial Neural Networks (ANN) and Support Vector Machines (SVM)
  4. 5.3 Linear Regression and Logistic Regression
  5. 5.4 Demonstration of Linear Regression with Python and KNIME
• Lesson 6: Text Analytics and Text Mining
  1. Topics
  2. 6.1 Introduction to Text Mining and Natural Language Processing
  3. 6.2 Text Mining Applications and Text Mining Process
  4. 6.3 Text Mining Tools and Demonstration of Text Mining Using Rapid Miner
  5. 6.4 Text Mining Tools and Demonstration of Sentiment Analysis and Topic Modeling with KNIME
• Lesson 7: Big Data Analytics
  1. Topics
  2. 7.1 What Is Big Data and Where Does It Come From?
  3. 7.2 Fundamental Concepts and Technologies of Big Data
  4. 7.3 Who Are Data Scientists, and Where Do They Come From?
  5. 7.4 Demonstration of Big Data Analytics (SAS Visual Analytics)
• Lesson 8: Predictive Analytics Best Practices
  1. Topics
  2. 8.1 Defining Model Ensembles and Their Pros and Cons
  3. 8.2 Bias-Variance Tradeoff in Predictive Analytics
  4. 8.3 Treating the Data-Imbalance Problem with Over- and Undersampling
  5. 8.4 Explainable ML/AI/Predictive Analytics
  6. 8.5 Showcasing Better Practices with a Comprehensive Model of Customer Churn Analysis
• Summary
  1. Predictive Analytics: Summary

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