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R Data Science Essentials [Paperback]

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  • Category: Books (Computers)
  • Author:  Raja B. Koushik, Sharan Kumar Ravindran
  • Author:  Raja B. Koushik, Sharan Kumar Ravindran
  • ISBN-10:  1785286544
  • ISBN-10:  1785286544
  • ISBN-13:  9781785286544
  • ISBN-13:  9781785286544
  • Publisher:  Packt Publishing - ebooks Account
  • Publisher:  Packt Publishing - ebooks Account
  • Pages:  154
  • Pages:  154
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Apr-2016
  • Pub Date:  01-Apr-2016
  • SKU:  1785286544-11-MPOD
  • SKU:  1785286544-11-MPOD
  • Item ID: 102114900
  • Seller: ShopSpell
  • Ships in: 2 business days
  • Transit time: Up to 5 business days
  • Delivery by: Dec 26 to Dec 28
  • Notes: Brand New Book. Order Now.

Key Features

  • Become a pro at making stunning visualizations and dashboards quickly and without hassle
  • For better decision making in business, apply the R programming language with the help of useful statistical techniques.
  • From seasoned authors comes a book that offers you a plethora of fast-paced techniques to detect and analyze data patterns

Book Description

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.

What you will learn

  • Perform data preprocessing and basic operations on data
  • Implement visual and non-visual implementation data exploration techniques
  • Mine patterns from data using affinity and sequential analysis
  • Use different clustering algorithms and visualize them
  • Implement logistic and linear regression and find out how to evaluate and improve the performance of an algorithm
  • Extract patterns through visualization and build a forecasting algorithm
  • Build a recommendation engine using different collablcˇ
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