Beginning Data Science with Python and Jupyter : Use Powerful Industry-Standard Tools Within Jupyter and the Python Ecosystem to Unlock New, Actionable Insights from Your Data.

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.

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll.

Description based on publisher supplied metadata and other sources.

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