This is the code repository for Hands-On Data Science with R, published by Packt.
Techniques to perform data manipulation and mining to build smart analytical models using R
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
life <- fread("ch1_life_exp.csv", header=T)
# View contents of life
head(life)
Following is what you need for this book: 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
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | RStudio | Windows, macOS, and Linux (Any) |
| 13 | Microsoft Azure |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
Click here if you have any feedback or suggestions.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.