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Hands-On Data Science with Anaconda

By : Yuxing Yan, Yan

2.6 (5)

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Ecosystem of Anaconda

Ecosystem of Anaconda

Introduction

Miniconda

Anaconda Cloud

Finding help

Summary

Review questions and exercises

Anaconda Installation

Anaconda Installation

Installing Anaconda

Testing Python

Using IPython

Using Python via Jupyter

Introducing Spyder

Installing R via Conda

Installing Julia and linking it to Jupyter

Installing Octave and linking it to Jupyter

Finding help

Summary

Review questions and exercises

Data Basics

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

Data Visualization

Importance of data visualization

Data visualization in R

Data visualization in Python

Data visualization in Julia

Drawing simple graphs

Visualization packages for R

Visualization packages for Python

Visualization packages for Julia

Dynamic visualization

Summary

Review questions and exercises

Statistical Modeling in Anaconda

Statistical Modeling in Anaconda

Introduction to linear models

Running a linear regression in R, Python, Julia, and Octave

Critical value and the decision rule

F-test, 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

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

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

Unsupervised Learning in Anaconda

Introduction to unsupervised learning

Hierarchical clustering

k-means clustering

Introduction to Python packages – scipy

Introduction to Python packages – contrastive

Introduction to Python packages – sklearn (scikit-learn)

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

Supervised Learning in Anaconda

A glance at supervised learning

Classification

Implementation of supervised learning via R

Implementation via Python

Implementation via Octave

Implementation via Julia

Summary

Review questions and exercises

Predictive Data Analytics – Modeling and Validation

Predictive Data Analytics – Modeling and Validation

Understanding predictive data analytics

Useful datasets

Predicting future events

Model selection

Granger causality test

Summary

Review questions and exercises

Anaconda Cloud

Anaconda Cloud

Introduction to Anaconda Cloud

Jupyter Notebook in depth

Replicating others' environments locally

Summary

Review questions and exercises

Distributed Computing, Parallel Computing, and HPCC

Distributed Computing, Parallel Computing, and HPCC

Introduction to distributed versus parallel computing

Understanding MPI

Parallel processing in Python

Compute nodes

Anaconda add-on

Introduction to HPCC

Summary

Review questions and exercises

References

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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Using IPython

For a Windows version, after navigating to Anaconda3 from All Programs and then clicking on IPython, we can see the following:

You can also do the the same by launching Anaconda Prompt and then typing ipython.

This tells us that the version of Python is 3.6.3 and that the version of IPython is 6.1.0. We could type a few commands to test it. For example, if we invest $100 today for an investment horizon of 5 years, and the annual return is 10%, what is our expected future value? The future value formula for a given present value is provided here:

where FV is the future value, PV is the present value, R is the period return, and n is the number of periods:

The first line of command of pv=100 assigns a value of 100 to pv. For the fifth line, we typed a command line of pv*(1+r)^n. However, we received an error message of TypeError: unsupported operand type(s) for...

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