Predictive Analytics: The Secret to Predicting Future Events Using Big Data and Data Science Techniques Such as Data Mining, Predictive Modelling, Statistics, Data Analysis, and Machine Learning
by Richard Hurley  (Author)  

About this Book
If you want to learn about predictive analytics without having to read a boring textbook, then keep reading...

Companies are collecting more data from ever. With the ease of collecting all that data, all the different sources where you can receive the data, and the inexpensive storage, it makes sense to collect as much data as possible. But without a good analysis of that data, and without some time to really figure out what trends and insights are inside all of it, that data becomes worthless.

This is where predictive analytics is going to come in handy. You will be able to actually take all of the data that you have been collecting and storing, and see what insights are in there to lead some of your business decisions in the future.

This guidebook is going to look at predictive analytics, and some of the topics we will explore concerning this topic include:

• The basics of predictive analysis.
• How to predict events that are going to happen in the future with big data and data mining.
• How to predict events that are going to happen in the future with the help of data analysis and statistics.
• A look at machine learning and how this process can help make predictions.
• How to avoid prediction traps, avoid bias, and make the best decisions with this analysis.
• Some of the top reasons to implement this kind of analysis in your business.
• The steps you can take to create your own predictive analysis model.
• And much, much more!
  

Working on predictive analytics is going to be one of the best ways that your business can use the data you have to look more deeply inside, and sort through the different predictions you can make.

Brief Contents
Chapter 1. Introduction to Predictive Analytics
Chapter 2. Introduction to Predictive Analytics and Data Mining
Chapter 3. Standardized Processes for Predictive Analytics
Chapter 4. Data and Methods for Predictive Analytics
Chapter 5. Algorithms for Predictive Analytics
Chapter 6. Advanced Topics in Predictive Modeling
Chapter 7. Text Analytics, Topic Modeling, and Sentiment Analysis
Chapter 8. Big Data for Predictive Analytics
Chapter 9. Deep Learning and Cognitive Computing
Appendix A. KNIME and the Landscape of Tools for Business Analytics and Data Science

ASIN : B083CSL2Q1
Publisher : Pearson. 1st ed. 2020 edition (December 31, 2019)
Language : English
Pages : 80