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  • Book Overview & Buying R Data Science Essentials
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R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
3 (3)
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
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9
Index

Chapter 3. Pattern Discovery

Pattern discovery is an important concept in the field of data science. The ability to recognize a pattern is a very essential skill set for a data science professional to make accurate decisions. Though the pattern discovery skill generally comes with past experience, common sense, and intuition, there are also ways to extract it from the dataset.

The simplest way to know about the patterns in the dataset would be to visualize the data and look out for patterns in it. This method is very appropriate for the time series data to know about the seasonality and trends hidden in it.

As we have already covered visualizing data in R in the previous chapter, we will focus only on learning affinity analysis, which looks out for the co-occurrence of events in the dataset that would be impossible to know from just visualizing the data or browsing through it. Additionally, we will study about the sequence analysis where we will analyze various sequences of events...

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