Beginning data analysis with python and Jupyter : use powerful industry-standard tools to unlock new, actionable insight from your existing data

Beginning data analysis with python and Jupyter use powerful industry-standard tools to unlock new, actionable insight from your existing data

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. About This Book Get up and running with the Jupyter ecosystem and some example datasets Learn abou...

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Detalles Bibliográficos
Otros Autores: Galea, Alex, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing [2018]
Edición:1st edition
Materias:
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630737006719
Tabla de Contenidos:
  • Intro
  • Preface
  • Jupyter Fundamentals
  • Basic Functionality and Features
  • Subtopic A: What is a Jupyter Notebook and Why is it Useful?
  • Subtopic B: Navigating the Platform
  • Introducing Jupyter Notebooks
  • Subtopic C: Jupyter Features
  • Explore some of Jupyter's most useful features
  • Converting a Jupyter Notebook to a Python Script
  • Subtopic D: Python Libraries
  • Import the external libraries and set up the plotting environment
  • Our First Analysis - The Boston Housing Dataset
  • Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame
  • Load the Boston housing dataset
  • Subtopic B: Data Exploration
  • Explore the Boston housing dataset
  • Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks
  • Linear models with Seaborn and scikit-learn
  • Activity B: Building a Third-Order Polynomial Model
  • Subtopic D: Using Categorical Features for Segmentation Analysis
  • Create categorical fields from continuous variables and make segmented visualizations
  • Summary
  • Data Cleaning and Advanced Machine Learning
  • Preparing to Train a Predictive Model
  • Subtopic A: Determining a Plan for Predictive Analytics
  • Subtopic B: Preprocessing Data for Machine Learning
  • Explore data preprocessing tools and methods
  • Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem
  • Training Classification Models
  • Subtopic A: Introduction to Classification Algorithms
  • Training two-feature classification models with scikit-learn
  • The plot_decision_regions Function
  • Training k-nearest neighbors for our model
  • Training a Random Forest
  • Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves
  • Using k-fold cross validation and validation curves in Python with scikit-learn
  • Subtopic C: Dimensionality Reduction Techniques.
  • Training a predictive model for the employee retention problem
  • Summary
  • Web Scraping and Interactive Visualizations
  • Scraping Web Page Data
  • Subtopic A: Introduction to HTTP Requests
  • Subtopic B: Making HTTP Requests in the Jupyter Notebook
  • Handling HTTP requests with Python in a Jupyter Notebook
  • Subtopic C: Parsing HTML in the Jupyter Notebook
  • Parsing HTML with Python in a Jupyter Notebook
  • Activity A: Web Scraping with Jupyter Notebooks
  • Interactive Visualizations
  • Subtopic A: Building a DataFrame to Store and Organize Data
  • Building and merging Pandas DataFrames
  • Subtopic B: Introduction to Bokeh
  • Introduction to interactive visualizations with Bokeh
  • Activity B: Exploring Data with Interactive Visualizations
  • Summary
  • Index.

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