Name: Hands-On Data Science with Anaconda
Author: Dr. Yuxing Yan James Yan
ISBN: 9781788834735

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Hands-On Data Science with Anaconda

Dr. Yuxing Yan James Yan

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Dedication
Packt Upsell
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PacktPub.com
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
Get in touch
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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Hands-On Data Science with Anaconda