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

Description

Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook's functionality to understand how data science can be applied to solve real-world data problems. Key FeaturesGain useful insights into data science and machine learningExplore the different functionalities and features of a Jupyter NotebookDiscover how Python libraries are used with Jupyter for data analysisBook Description From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You'll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you'll start by getting to grips with Jupyter functionality and features. You'll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you'll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you'll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you'll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects. What you will learnUnderstand the key opportunities and challenges in data scienceUse Jupyter for data science tasks such as data analysis and modelingRun exploratory data analysis within a Jupyter NotebookVisualize data with pairwise scatter plots and segmented distributionAssess model performance with advanced validation techniquesParse HTML responses and analyze HTTP requestsWho This Book Is For If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory. 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