Over the years, researchers or users have generated many packages around different specific tasks for various programming languages. For this book, we treat module or toolbox as a synonym for package. For the analyses in the area of data science, it is very important to use various packages to achieve our goals. There are several advantages in using various packages. First, we don't have to write our code from scratch if we can find some relevant programs contained in certain packages. This would save us a huge amount of time. In other words, we don't have to reinvent the wheel, and this is especially true for developers. Second, packages are usually developed by people who have certain expertise in relevant areas. Because of this, the quality of a package is usually higher than the programs written by a, relatively speaking...
Hands-On Data Science with Anaconda
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Hands-On Data Science with Anaconda
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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)
Preface
Free Chapter
Ecosystem of Anaconda
Anaconda Installation
Data Basics
Data Visualization
Statistical Modeling in Anaconda
Managing Packages
Optimization in Anaconda
Unsupervised Learning in Anaconda
Supervised Learning in Anaconda
Predictive Data Analytics – Modeling and Validation
Anaconda Cloud
Distributed Computing, Parallel Computing, and HPCC
References
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