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Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners

Discover how to make the most of data with our Predictive Analytics, Data Mining, and Machine Learning course.

(PRED-ANA.AP1) / ISBN : 978-1-64459-326-4
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About This Course

This Predictive Analytics course teaches practitioners how data can work magic in decision-making, We’ll guide you through essential topics like data preprocessing, algorithms basics, and big data, all while keeping it fun and engaging. You’ll explore powerful tools for text analytics and sentiment analysis, learn how to create decision trees, and even get hands-on with popular techniques like k-means clustering. Use what you learn to turn data into real-world results that dazzle and impress.

Skills You’ll Get

  • Learn various methods for discovering patterns and insights in data 
  • Gain the ability to create and validate models that predict future outcomes
  • Apply popular algorithms, such as k-nearest Neighbor, Naive Bayes, and linear regression 
  • Classify tasks by creating and analyzing decision trees 
  • Use k-means clustering and understand its applications 
  • Exploring text analytics and sentiment analysis to extract insights from textual data 
  • Learn how to assess model performance and make improvements 
  • Develop skills to visually present data and analyze results effectively 
  • Gain practical experience with tools like KNIME and Python for data analysis
Download Course Outline

1

Introduction

  • About This eBook
  • Foreword
2

Introduction to Analytics

  • What’s in a Name?
  • Why the Sudden Popularity of Analytics and Data Science?
  • The Application Areas of Analytics
  • The Main Challenges of Analytics
  • A Longitudinal View of Analytics
  • A Simple Taxonomy for Analytics
  • The Cutting Edge of Analytics: IBM Watson
  • Summary
  • References
3

Introduction to Predictive Analytics and Data Mining

  • What Is Data Mining?
  • What Data Mining Is Not
  • The Most Common Data Mining Applications
  • What Kinds of Patterns Can Data Mining Discover?
  • Popular Data Mining Tools
  • The Dark Side of Data Mining: Privacy Concerns
  • Summary
  • References
4

Standardized Processes for Predictive Analytics

  • The Knowledge Discovery in Databases (KDD) Process
  • Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • SEMMA
  • SEMMA Versus CRISP-DM
  • Six Sigma for Data Mining
  • Which Methodology Is Best?
  • Summary
  • References
5

Data and Methods for Predictive Analytics

  • The Nature of Data in Data Analytics
  • Preprocessing of Data for Analytics
  • Data Mining Methods
  • Prediction
  • Classification
  • Decision Trees
  • Cluster Analysis for Data Mining
  • k-Means Clustering Algorithm
  • Association
  • Apriori Algorithm
  • Data Mining and Predictive Analytics Misconceptions and Realities
  • Summary
  • References
6

Algorithms for Predictive Analytics

  • Naive Bayes
  • Nearest Neighbor
  • Similarity Measure: The Distance Metric
  • Artificial Neural Networks
  • Support Vector Machines
  • Linear Regression
  • Logistic Regression
  • Time-Series Forecasting
  • Summary
  • References
7

Advanced Topics in Predictive Modeling

  • Model Ensembles
  • Bias–Variance Trade-off in Predictive Analytics
  • Imbalanced Data Problems in Predictive Analytics
  • Explainability of Machine Learning Models for Predictive Analytics
  • Summary
  • References
8

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Natural Language Processing
  • Text Mining Applications
  • The Text Mining Process
  • Text Mining Tools
  • Topic Modeling
  • Sentiment Analysis
  • Summary
  • References
9

Big Data for Predictive Analytics

  • Where Does Big Data Come From?
  • The Vs That Define Big Data
  • Fundamental Concepts of Big Data
  • The Business Problems That Big Data Analytics Addresses
  • Big Data Technologies
  • Data Scientists
  • Big Data and Stream Analytics
  • Data Stream Mining
  • Summary
  • References
10

Deep Learning and Cognitive Computing

  • Introduction to Deep Learning
  • Basics of “Shallow” Neural Networks
  • Elements of an Artificial Neural Network
  • Deep Neural Networks
  • Convolutional Neural Networks
  • Recurrent Networks and Long Short-Term Memory Networks
  • Computer Frameworks for Implementation of Deep Learning
  • Cognitive Computing
  • Summary
  • References
A

Appendix A: KNIME and the Landscape of Tools for Business Analytics and Data Science

  • Project Constraints: Time and Money
  • The Learning Curve
  • The KNIME Community
  • Correctness and Flexibility
  • Extensive Coverage of Data Science Techniques
  • Data Science in the Enterprise
  • Summary and Conclusions
  • Acknowledgment
B

Appendix B: Videos

  • Introduction to Predictive Analytics
  • Introduction to Predictive Analytics and Data Mining
  • The Data Mining Process
  • Data and Methods in Data Mining
  • Data Mining Algorithms
  • Text Analytics and Text Mining
  • Big Data Analytics
  • Predictive Analytics Best Practices
  • Summary

1

Introduction to Predictive Analytics and Data Mining

  • Creating a Decision Tree in Python
  • Creating a Decision Tree in KNIME
2

Data and Methods for Predictive Analytics

  • Running k-Means Clustering Algorithm in KNIME
3

Algorithms for Predictive Analytics

  • Using the k-Nearest Neighbor Algorithm
  • Using ANN and SVM for Prediction Type Analytics Problems
  • Implementing Linear Regression in Python
  • Implementing Linear Regression Model in KNIME
4

Advanced Topics in Predictive Modeling

  • Showcasing Better Practices With a Customer Churn Analysis
5

Text Analytics, Topic Modeling, and Sentiment Analysis

  • Performing Topic Modeling
  • Performing Sentiment Analysis

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Predictive analytics in data mining involves using statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. It helps uncover patterns and trends to make informed predictions.

Data mining is the process of discovering patterns and knowledge from large amounts of data.

Predictive analytics use ML and statistical algorithms to predict and plan for future outcomes. 

Machine learning involves using algorithms to extract and transform information into an understandable structure for further use.

With skills in predictive analytics, data mining, and machine learning, you can work in various industries, including finance, healthcare, marketing, retail, manufacturing, and technology.

This predictive business analytics course is ideal for anyone interested in data analytics, business intelligence, or machine learning.

Yes, this predictive analytics training course provides a solid foundation in data science principles, making it possible to transition into a data science career.

This predictive analytics and machine learning course equips you with in-demand skills in analytics and data science, making you a more competitive candidate in the job market and opening doors to new opportunities.

No prior knowledge is required! Our best predictive analytics course is designed for both beginners and practitioners.

Predictive analytics helps businesses forecast trends, understand customer behavior, optimize operations, and make data-driven decisions. This leads to better efficiency, higher profits, and a competitive advantage in the market.

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