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)

Ecosystem of Anaconda

In the preface, we mentioned that this book is designed for readers who are looking for tools in the area of data science. Existing data analysts and data science professionals who wish to improve the efficiency of their data science applications by using the best libraries with multiple languages will find this book quite useful. The platform discussed in detail across various chapters is Anaconda and the computational tools could be Python, R, Julia, or Octave. The beauty of using these programming languages is that they are all open source, as in free to download. In this chapter, we start from the very beginning: a simple introduction. For this book, we assume that readers have some basic knowledge related to several programming languages, such as R and Python. There are many books available, such as Python for Data Analysis by McKinney (2013) and Python for Finance by Yan (2017).

In this chapter, the following topics will be covered:

  • Introduction
  • Miniconda
  • Anaconda Cloud
  • Finding help