Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications

April 26, 2019 Books
Hands-On Data Science with Anaconda: Utilize the right mix of tools to create high-performance data science applications

English | 2018 | ISBN: 978-1788831192 | 364 Pages | PDF, EPUB | 35 MB

Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda
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.
What you will learn

  • Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
  • Use the package manager conda and discover, install, and use functionally efficient and scalable packages
  • Get comfortable with heterogeneous data exploration using multiple languages within a project
  • Perform distributed computing and use Anaconda Accelerate to optimize computational powers
  • Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
  • Tackle advanced data prediction problems
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