Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Finding manuals

For an R package, the best way to find the manual is to find the location of the installed R package. In the following example, we use the R package called rattle as an example:

> library(rattle) 
> path.package('rattle') 
[1] "C:/Users/yany/Documents/R/win-library/3.3/rattle" 

Note that different readers will definitively get different paths. Our result is shown in the following screenshot:

The PDF manual and HTML manuals are located under the doc subdirectory. It is a great idea to explore these subdirectories. To save space, we will not show the detailed files contained under the subdirectories. The second best way is to go to http://r-project.org, click CRAN, choose a nearby mirror location and click packages on the left-hand side. Then, from one of the two lists, search for the package. After clicking on the package, we will find...