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)

Understanding MPI

Usually, a parallel algorithm needs to move data between different engines. One way to do so is by doing a pull and then a push using the direct view. However, this method is quite slow since all the data has to go through the controller to the client and then back through the controller, to its final destination. A much better way of moving data between engines is to use a message passing library, such as the Message Passing Interface (MPI). IPython's parallel computing architecture has been designed to integrate with MPI. To download and install Windows MPI, readers can refer to https://msdn.microsoft.com/en-us/library/bb524831%28v=vs.85%29.aspx.

In addition, you could install the mpi4py package.

R package Rmpi

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