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  • Hands-On Data Science with Anaconda
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Hands-On Data Science with Anaconda

Hands-On Data Science with Anaconda

By : Yuxing Yan, Yan
2.6 (5)
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Hands-On Data Science with Anaconda

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.
Table of Contents (15 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Ecosystem of Anaconda
Ecosystem of Anaconda
Introduction
Miniconda
Anaconda Cloud
Finding help
Summary
Review questions and exercises
2
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
3
Data Basics
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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
4
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
5
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
6
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
7
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
8
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
9
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
10
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
11
Anaconda Cloud
Anaconda Cloud
Introduction to Anaconda Cloud
Jupyter Notebook in depth
Replicating others' environments locally
Summary
Review questions and exercises
12
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
13
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
14
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Introduction to the dslabs R package

The dslabs R package is short for Data Science Labs. The package includes several datasets, such as the dataset called murders for US gun murders by state for 2010:

> library(dslabs) 
> data(murders) 
> head(murders) 
       state abb region population total 
1    Alabama  AL  South    4779736   135 
2     Alaska  AK   West     710231    19 
3    Arizona  AZ   West    6392017   232 
4   Arkansas  AR  South    2915918    93 
5 California  CA   West   37253956  1257 
6   Colorado  CO   West    5029196    65

The following table shows the datasets included in the package:

Name of dataset
Description

admissions

Gender bias among graduate school admissions to UC Berkeley

divorce_margarine

Divorce rate and margarine consumption data

ds_theme_set

dslabs theme set

gapminder

Gapminder data

heights

Self-Reported...

End of Section 12
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