Nowadays, one of the big buzzwords in computing is cloud. The simplest example is that for many users, Google Drive is a kind of cloud where users can save their files, photos, and programs and share them with their colleagues, family members, or others. Another example is so-called cloud computing. According to the National Institute of Standards and Technology, cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (for example, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. In Old English, mass of rock, hill, was related to cloud. The metaphoric extension to raincloud, mass of evaporated water in the sky, was attested by c. 1200, based on the similarity of cumulus clouds...
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.
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Hands-On Data Science with Anaconda
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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Customer Reviews