Data analytics using R
Material type:
TextPublication details: New Dellhi McGraw Hill Education (India) Pvt. Ltd. 2018 Description: ix, 555 pISBN: 9789352605248Subject(s): Data analytics | R ProgrammingDDC classification: 005.133 Summary: his 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’.
• Exhaustivecoverage includes installation of R and its package, getting accustomed to Rinterface and R commands, working with data from disparate data sources (.csv,JSON, XML, RDBMS etc.), getting conversant with classification, clustering,association rule mining, regression, text mining etc.
• 12 Casestudies namely Insurance Fraud Detection, Customer Insights Analysis, SalesForecasting, Credit Card Spending by Customer Groups and Helping RetailersPredict In-store Customer Traffic
• Pedagogy
o 300+chapter-end and check your progress questions for self-assessment
o 200Multiple-choice questions
o 10+hands-on practical exercises
o Exhaustiveillustrations
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Book
|
Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 005.133 ACH (Browse shelf(Opens below)) | 1 | Available | 000663 |
his 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’.
• Exhaustivecoverage includes installation of R and its package, getting accustomed to Rinterface and R commands, working with data from disparate data sources (.csv,JSON, XML, RDBMS etc.), getting conversant with classification, clustering,association rule mining, regression, text mining etc.
• 12 Casestudies namely Insurance Fraud Detection, Customer Insights Analysis, SalesForecasting, Credit Card Spending by Customer Groups and Helping RetailersPredict In-store Customer Traffic
• Pedagogy
o 300+chapter-end and check your progress questions for self-assessment
o 200Multiple-choice questions
o 10+hands-on practical exercises
o Exhaustiveillustrations
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