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Hands-On Data Science with Anaconda
Hands-OnDataSciencewithAnacondaisforyouifyouareadeveloperwhoislookingforthebesttoolsinthemarkettoperformdatascience.It’salsoidealfordataanalystsanddatascienceprofessionalswhowanttoimprovetheefficiencyoftheirdatascienceapplicationsbyusingthebestlibrariesinmultiplelanguages.BasicprogrammingknowledgewithRorPythonandintroductoryknowledgeoflinearalgebraisexpected.
目錄(221章)
倒序
- 封面
- 版權(quán)信息
- Dedication
- Packt Upsell
- Why subscribe?
- 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
- Other Books You May Enjoy
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