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Data analytics using R

By: Acharya, Seema.
Publisher: Chennai McGraw Hill Education 2018Description: xxiii, 555p. | Binding - Paperback | 24.2*18.5 cm.ISBN: 9789352605248.Subject(s): Computer EngineeringDDC classification: 006.312 Summary: This book is aimed at undergraduate students of computerscience and engineering. The book will be useful companion for IT professionalsto data analysts and decision makers responsible for driving strategicinitiatives, and management graduates and business analysts, engaged inself-study. This book by Acharya unleashes the power of R as astatistical data analytics and visualization tool and introduces the learnersto several data mining algorithms and chart forms / visualizations. It has goodemphasis on ‘asking the right questions’. CONTENT: Chapter 1 Introduction to R Chapter 2 Getting Started with R Chapter 3 Loading and Handling Data in R Chapter 4 Exploring Data in R Chapter 5 Linear Regression using R Chapter 6 Logistic Regression Chapter 7 Decision Tree Chapter 8 Time Series in R Chapter 9 Clustering Chapter 10 Association Rules Chapter 11 Text Mining Chapter 12 Parallel Computing with R
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006.31 MIT Machine learning 006.31 OSI Deep learning cookbook 006.312 ACH Data analytics using R 006.312 ACH Data analytics using R 006.312 ACH Data analytics using R 006.312 ACH Data analytics using R 006.312 BPB Data Mining

This book is aimed at undergraduate students of computerscience and engineering. The book will be useful companion for IT professionalsto data analysts and decision makers responsible for driving strategicinitiatives, and management graduates and business analysts, engaged inself-study.

This book by Acharya unleashes the power of R as astatistical data analytics and visualization tool and introduces the learnersto several data mining algorithms and chart forms / visualizations. It has goodemphasis on ‘asking the right questions’.
CONTENT:
Chapter 1 Introduction to R

Chapter 2 Getting Started with R

Chapter 3 Loading and Handling Data in R

Chapter 4 Exploring Data in R

Chapter 5 Linear Regression using R

Chapter 6 Logistic Regression

Chapter 7 Decision Tree

Chapter 8 Time Series in R

Chapter 9 Clustering

Chapter 10 Association Rules

Chapter 11 Text Mining

Chapter 12 Parallel Computing with R

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