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)

Summary

In this chapter, we showed you how to install Anaconda and test if the installation was successful. We looked at how to launch Jupyter and how to use it to launch Python, how to launch Spyder and R, and how to find help. Most of these concepts or procedures are quite basic, so users who are quite confident with them can skip this chapter and go to Chapter 3, Data Basics, directly.

In Chapter 3, Data Basics, we first discuss open data sources such as the UCI (University of California at Irvin) machine learning depository and the bureau of labor statistics. Then, we introduce the Python Pandas package. Many issues, such as how to deal with missing data, sorting, how to slice and dice datasets, merging different datasets, and data input and output, will be discussed in detail. Several relevant packages for data manipulation will be also introduced and discussed.

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