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)

Miniconda

Anaconda is a full distribution of Python and comes with over 1,000 open source packages after installation. Because of this, the total size is over 3 GB. Anaconda is good if we intend to have many packages downloaded and pre-installed. On the other hand, Miniconda contains only Python and other necessary libraries needed to run conda itself. The size for the Miniconda is about 400 MB, much smaller than the full version of Anaconda, so extra packages have to be downloaded and installed as requested.

There are many reasons why a new user might prefer a watered-down version of Anaconda. For example, they might not need so many packages. Another reason is that users might not have enough space. Those users could download Miniconda at https://conda.io/miniconda.html. Again, in Chapter 2, Anaconda Installation, we will discuss in detail how to install Anaconda and run programs written in different languages, such as Python, R, Julia, and Octave.