Hands-On Data Science with R

Hands-On Data Science with R

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Product Details

ISBN-13: 9781789139402
Publisher: Packt Publishing
Publication date: 11/30/2018
Pages: 420
Product dimensions: 7.50(w) x 9.25(h) x 0.86(d)

About the Author

Vitor Bianchi Lanzetta (@vitorlanzetta) has a master's degree in Applied Economics (University of São Paulo—USP) and works as a data scientist in a tech start-up named RedFox Digital Solutions. He has also authored a book called R Data Visualization Recipes. The things he enjoys the most are statistics, economics, and sports of all kinds (electronics included). His blog, made in partnership with Ricardo Anjoleto Farias (@R_A_Farias), can be found at ArcadeData dot org, they kindly call it R-Cade Data. Nataraj Dasgupta is the vice president of advanced analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years, and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank, and Purdue Pharma. At Purdue Pharma, Nataraj led the data science division, where he developed the company's award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of Associate Director, working with high-frequency and algorithmic trading technologies in the foreign exchange trading division of the bank. Ricardo Anjoleto Farias is an economist who graduated from the Universidade Estadual de Maringá in 2014. In addition to being a sports enthusiast (electronic or otherwise) and enjoying a good barbecue, he also likes math, statistics, and correlated studies. His first contact with R was when he embarked on his master's degree, and since then, he has tried to improve his skills with this powerful tool.

Table of Contents

Table of Contents

  1. Getting started withData Science and R
  2. Descriptive and Inferential Statistics
  3. Data Wrangling with R
  4. KDD, Data Mining, and Text Mining
  5. Data Analysis with R
  6. Machine Learning with R
  7. Forecasting and ML App with R
  8. Neural Networks and Deep Learning
  9. Markovian in R
  10. Visualizing Data
  11. Going to Production with R
  12. Large Scale Data Analytics with Hadoop
  13. R on Cloud
  14. The Road Ahead

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