# Franklin

### Staff View

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041218s2018 xx o eng

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a| (CKB)4970000000061293

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a| (CaSebORM)9781789139402

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a| eng

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a| Lanzetta, Vitor,
e| author.

245

1

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a| Hands-On Data Science with R
h| [electronic resource] /
c| Lanzetta, Vitor.

250

a| 1st edition

264

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b| Packt Publishing,
c| 2018.

300

a| 1 online resource (420 pages)

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a| text
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a| online resource
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a| text file

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a| Online resource; Title from title page (viewed November 30, 2018)

520

a| A hands-on guide for professionals to perform various data science tasks in R Key Features Explore the popular R packages for data science Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on examples and use-cases in R Book Description R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity. What you will learn Understand the R programming language and its ecosystem of packages for data science Obtain and clean your data before processing Master essential exploratory techniques for summarizing data Examine various machine learning prediction, models Explore the H2O analytics platform in R for deep learning Apply data mining techniques to available datasets Work with interactive visualization packages in R Integrate R with Spark and Hadoop for large-scale data analytics Who this book is for If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

655

4

a| Electronic books.

776

z| 1-78913-940-6

700

1

a| Dasgupta, Nataraj,
e| author.

700

1

a| Farias, Ricardo,
e| author.

906

a| BOOK