Hands-On Data Science with Anaconda : Utilize the right mix of tools to create high-performance data science applications /

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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Yan, Yuxing (Autor)
Otros Autores: Yan, James
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, 2018.
Temas:
ANACONDA (Electronic resource)
Machine learning.
Information visualization.
Electronic data processing.
Apprentissage automatique.
Visualisation de l'information.
Programming & scripting languages: general.
Mathematical theory of computation.
Information architecture.
Database design & theory.
Computers > Machine Theory.
Computers > Programming Languages > Python.
Computers > Data Modeling & Design.
COMPUTERS > General.
Electronic data processing
Information visualization
Machine learning
Acceso en línea:Texto completo
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.

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