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 have first discussed the importance of managing packages. Then, we have shown how to find all the available packages for R, Python, Julia, and Octave, how to install and update individual packages, and how to find the manual for teaching the packages. In addition, we have explained the issue of package dependencies and how to make our programming a little easier when dealing with packages. The topic of systematic environment was touched on as well.

In Chapter 7, Optimization in Anaconda, we will discuss several topics around optimization, such as general issues for optimization problems and expressing various kinds of optimization problems (for example, LP and quadratic optimization). Several examples are offered to make our discussion more practice-oriented, such as how to choose an optimal stock portfolio and how to optimize wealth and resources to...