Hands-On Data Science with Anaconda : Utilize the right mix of tools to create high-performance data science applications.
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; Dat...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing,
2018.
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| Online Access: | CONNECT |
MARC
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| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 505 | 8 | |a Generating R datasetsSummary; 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. | |
| 505 | 8 | |a Chapter 5: Statistical Modeling in AnacondaIntroduction 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. | |
| 505 | 8 | |a Introduction to packages, modules, or toolboxesTwo 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. | |
| 500 | |a General issues for optimization problems. | ||
| 520 | |a 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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