Book description
Develop, deploy, and streamline your data science projects with the most popular endtoend platform, Anaconda
About This Book Use Anaconda to find solutions for clustering, classification, and linear regression
 Analyze your data efficiently with the most powerful data science stack
 Use the Anaconda cloud to store, share, and discover projects and libraries
HandsOn Data Science with Anaconda is for you if you are a developer who is looking for the best tools in the market to perform data science. It's also ideal for data analysts and data science professionals who want to improve the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.
What You Will Learn Perform cleaning, sorting, classification, clustering, regression, and dataset modeling using Anaconda
 Use the package manager conda and discover, install, and use functionally efficient and scalable packages
 Get comfortable with heterogeneous data exploration using multiple languages within a project
 Perform distributed computing and use Anaconda Accelerate to optimize computational powers
 Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
 Tackle advanced data prediction problems
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. HandsOn 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 crosslanguage 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, scikitlearn, 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.
Style and approachThis book is your stepbystep guide full of use cases, examples and illustrations to get you wellversed with the concepts of Anaconda.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 Dedication
 Packt Upsell
 Contributors
 Preface
 Ecosystem of Anaconda
 Anaconda Installation

Data Basics
 Sources of data
 UCI machine learning
 Introduction to the Python pandas package
 Several ways to input data
 Introduction to the Quandl data delivery platform
 Dealing with missing data
 Data sorting
 Introduction to the cbsodata Python package
 Introduction to the datadotworld Python package
 Introduction to the haven and foreign R packages
 Introduction to the dslabs R package
 Generating Python datasets
 Generating R datasets
 Summary
 Review questions and exercises
 Data Visualization

Statistical Modeling in Anaconda
 Introduction to linear models
 Running a linear regression in R, Python, Julia, and Octave
 Critical value and the decision rule
 Ftest, critical value, and the decision rule
 Dealing with missing data
 Detecting outliers and treatments
 Several multivariate linear models
 Collinearity and its solution
 A model's performance measure
 Summary
 Review questions and exercises

Managing Packages
 Introduction to packages, modules, or toolboxes
 Two examples of using packages
 Finding all R packages
 Finding all Python packages
 Finding all Julia packages
 Finding all Octave packages
 Task views for R
 Finding manuals
 Package dependencies
 Package management in R
 Package management in Python
 Package management in Julia
 Package management in Octave
 Conda – the package manager
 Creating a set of programs in R and Python
 Finding environmental variables
 Summary
 Review questions and exercises

Optimization in Anaconda
 Why optimization is important
 General issues for optimization problems
 Quadratic optimization
 Example #1 – stock portfolio optimization
 Example #2 – optimal tax policy
 Packages for optimization in R
 Packages for optimization in Python
 Packages for optimization in Octave
 Packages for optimization in Julia
 Summary
 Review questions and exercises

Unsupervised Learning in Anaconda
 Introduction to unsupervised learning
 Hierarchical clustering
 kmeans clustering
 Introduction to Python packages – scipy
 Introduction to Python packages – contrastive
 Introduction to Python packages – sklearn (scikitlearn)
 Introduction to R packages – rattle
 Introduction to R packages – randomUniformForest
 Introduction to R packages – Rmixmod
 Implementation using Julia
 Task view for Cluster Analysis
 Summary
 Review questions and exercises
 Supervised Learning in Anaconda
 Predictive Data Analytics – Modeling and Validation
 Anaconda Cloud
 Distributed Computing, Parallel Computing, and HPCC

References
 Chapter 01: Ecosystem of Anaconda
 Chapter 02: Anaconda Installation
 Chapter 03: Data Basics
 Chapter 04: Data Visualization
 Chapter 05: Statistical Modeling in Anaconda
 Chapter 06: Managing Packages
 Chapter 07: Optimization in Anaconda
 Chapter 08: Unsupervised Learning in Anaconda
 Chapter 09: Supervised Learning in Anaconda
 Chapter 10: Predictive Data Analytics – Modelling and Validation
 Chapter 11: Anaconda Cloud
 Chapter 12: Distributed Computing, Parallel Computing, and HPCC
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Product information
 Title: HandsOn Data Science with Anaconda
 Author(s):
 Release date: May 2018
 Publisher(s): Packt Publishing
 ISBN: 9781788831192
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