Hands-On Data Science with Anaconda by Yan, Yuxing -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On Data Science with Anaconda

Dedication Packt Upsell

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Contributors

About the authors About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Reviews

Ecosystem of Anaconda

Introduction

Reasons for using Jupyter via Anaconda Using Jupyter without pre-installation

Miniconda Anaconda Cloud Finding help Summary Review questions and exercises

Anaconda Installation

Installing Anaconda

Anaconda for Windows

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

Sources of data UCI machine learning Introduction to the Python pandas package Several ways to input data

Inputting data using R Inputting data using Python

Introduction to the Quandl data delivery platform Dealing with missing data Data sorting

Slicing and dicing datasets Merging different datasets Data output

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

Importance of data visualization Data visualization in R Data visualization in Python Data visualization in Julia Drawing simple graphs

Various bar charts, pie charts, and histograms Adding a trend Adding legends and other explanations

Visualization packages for R Visualization packages for Python Visualization packages for Julia Dynamic visualization

Saving pictures as pdf Saving dynamic visualization as HTML file

Summary Review questions and exercises

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

An application of a linear regression in finance

Dealing with missing data

Removing missing data Replacing missing data with another value

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

Expressing various kinds of optimization problems as LPP

Quadratic optimization

Optimization in R Optimization in Python Optimization in Julia Optimization in Octave

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 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

A glance at supervised learning Classification

The k-nearest neighbors algorithm Bayes classifiers Reinforcement learning

Implementation of supervised learning via R

Introduction to RTextTools

Implementation via Python

Using the scikit-learn (sklearn) module

Implementation via Octave Implementation via Julia

Task view for machine learning in R

Summary Review questions and exercises

Predictive Data Analytics – Modeling and Validation

Understanding predictive data analytics Useful datasets

The AppliedPredictiveModeling R package Time series analytics

Predicting future events

Seasonality Visualizing components R package – LiblineaR R package – datarobot R package – eclust

Model selection

Python package – model-catwalk Python package – sklearn Julia package – QuantEcon Octave package – ltfat

Granger causality test Summary Review questions and exercises

Anaconda Cloud

Introduction to Anaconda Cloud Jupyter Notebook in depth

Formats of Jupyter Notebook Sharing of notebooks Sharing of projects Sharing of environments

Replicating others' environments locally

Downloading a package from Anaconda

Summary Review questions and exercises

Distributed Computing, Parallel Computing, and HPCC

Introduction to distributed versus parallel computing

Task view for parallel processing Sample programs in Python

Understanding MPI

R package Rmpi R package plyr R package parallel R package snow

Parallel processing in Python

Parallel processing for word frequency Parallel Monte-Carlo options pricing

Compute nodes Anaconda add-on Introduction to HPCC Summary Review questions and exercises

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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