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Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

By : Yuxing Yan, Yan
2.6 (5)
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Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

2.6 (5)
By: Yuxing Yan, 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)
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Creating a set of programs in R and Python

On numerous occasions, for a specific research topic, researchers will collect many datasets and write many programs. Why not write a big program? There are several reasons for not doing so. First, we might need several steps to finish the project. Second, the project might be too complex, so we have divided the whole project into several small portions. Each researcher will be responsible for one or a few portions. Third, according to the flow of the whole process, we might want to have several parts, such as devoting a part to processing data, a part to running various regressions, and a part to summarizing the results. Because of this, we need a way of putting all the programs together. In the following example, we will show you how to achieve this in both R and Python. For R, assume that we have the following functions:

pv_f<-function...
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