The Applied Data Science Workshop - Second Edition: Get started with the applications of data science and techniques to explore and assess data effectively

Cover
FM
Copyright
Table of Contents
Preface
Chapter 1: Introduction to Jupyter Notebooks
	Introduction
	Basic Functionality and Features of Jupyter Notebooks
		What Is a Jupyter Notebook and Why Is It Useful?
		Editing Notebooks with Jupyter Notebooks and JupyterLab
		Exercise 1.01: Introducing Jupyter Notebooks
		Exercise 1.02: Introducing the JupyterLab Platform
	Jupyter Features
		Exercise 1.03: Demonstrating the Core Jupyter Features
		Converting a Jupyter Notebook into a Python Script
		Python Libraries
		Activity 1.01: Using Jupyter to Learn about pandas DataFrames
	Summary
Chapter 2: Data Exploration with Jupyter
	Introduction
	Our First Analysis – the Boston Housing Dataset
		Exercise 2.01: Importing Data Science Libraries and Setting Up the Notebook Plotting Environment
		Loading the Data into Jupyter Using a pandas DataFrame
		Exercise 2.02: Loading the Boston Housing Dataset
		Data Exploration
		Exercise 2.03: Analyzing the Boston Housing Dataset
		Introduction to Predictive Analytics with Jupyter Notebooks
		Exercise 2.04: Training Linear Models with Seaborn and scikit-learn
		Using Categorical Features for Segmentation Analysis
		Exercise 2.05: Creating Categorical Fields from Continuous Variables and Making Segmented Visualizations
		Activity 2.01: Building a Third-Order Polynomial Model
	Summary
Chapter 3: Preparing Data for Predictive Modeling
	Introduction
	Machine Learning Process
	Approaching Data Science Problems
	Understanding Data from a Modeling Perspective
		Preparing Data for Modeling
		Exercise 3.01: Data Cleaning for Machine Learning with Pandas
		Exercise 3.02: Preparing Data for Machine Learning with Pandas
	Introducing the Human Resource Analytics Dataset
		Activity 3.01: Preparing to Train a Predictive Model for Employee Retention
	Summary
Chapter 4: Training Classification Models
	Introduction
	Understanding Classification Algorithms
		Exercise 4.01: Training Two-Feature Classification Models with Scikit-Learn
		The plot_decision_regions Function
		Exercise 4.02: Training k-Nearest Neighbors Classifiers with Scikit-Learn
		Exercise 4.03: Training Random Forest Classifiers with Scikit-Learn
		Activity 4.01: Training and Visualizing SVM Models with Scikit-Learn
	Summary
Chapter 5: Model Validationand Optimization
	Introduction
	Assessing Models with k-Fold Cross Validation
		Tuning Hyperparameters with Validation Curves
		Exercise 5.01: Using k-Fold Cross Validation and Validation Curves in Python with Scikit-Learn
	Dimensionality Reduction with PCA
		Exercise 5.02: Dimensionality Reduction with PCA
		Model Training for Production
		Exercise 5.03: Training a Production-Ready Model for Employee Turnover
		Activity 5.01: Hyperparameter Tuning and Model Selection
	Summary
Chapter 6: Web Scraping with Jupyter Notebooks
	Introduction
	Internet Data Sources
	Introduction to HTTP Requests
		Making HTTP Requests with Python
		Exercise 6.01: Using Python and Jupyter Notebooks to Make HTTP Requests
		Making API Calls with Python
		Exercise 6.02: Making API calls with Python and Jupyter Notebooks
		Parsing HTML with Jupyter Notebooks
		Exercise 6.03: Parsing HTML with Python and Jupyter Notebooks
		Activity 6.01: Web Scraping with Jupyter Notebook
	Data Workflow with pandas
		Exercise 6.04: Processing Data for Analysis with pandas
		Exercise 6.05: Merging Data with pandas
		Activity 6.02: Analyzing Country Populations and Interest Rates
	Summary
Appendix
Index