Hands-On Data Science with Anaconda : Utilize the Right Mix of Tools to Create High-Performance Data Science Applications.

Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: 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 -- Chapter 2: 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 -- Chapter 3: 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 -- Chapter 4: 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 -- Chapter 5: 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 -- Chapter 6: 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 -- Chapter 7: 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 -- Chapter 8: 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 -- Chapter 9: 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 -- Chapter 10: 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 -- Chapter 11: 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 -- Chapter 12: 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 -- Index.

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. You will learn different ways to retrieve data from various sources and different visualization tools packages available in Python, R, and Julia.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.