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...
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2018.
|
| Colección: | EBSCO Academic eBook Collection Complete.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b45003397*spi |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
