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)

Task view for Cluster Analysis

In the previous chapters, we have discussed various kinds of task views. A task view is a set of R packages organized by one or a few experts around a topic. For the Cluster Analysis, https://CRAN.R-project.org/view=Cluster, the top part is shown in the following screenshot:

As discussed in Chapter 6, Managing Packages, we can use just three lines of R code to install these cluster-related packages:

>install.packages("ctv") 
>library("ctv") 
>install.views("Cluster") 

In total, there are about 154 R packages included in the previous task view called Cluster, as of March 21, 2018.