Overview

""Web Scripting and API Integration for Data Science: A Practical Guide to Building Data-Driven Applications"" is a comprehensive and practical book designed specifically for data science students and professionals who want to master the essential web scripting and API skills needed to interact with, extract, and manipulate data from web sources and services. In today's data-driven world, data scientists are not only expected to analyze and interpret vast datasets but also to collect, integrate, and manage data from diverse sources, many of which reside on the web and are accessible through APIs (Application Programming Interfaces). This book bridges the gap between core data science knowledge and web technologies by empowering readers with the hands-on skills needed to build real-world data applications using web scripting and APIs. What This Book Covers This book is structured to guide readers step-by-step, starting from the fundamental concepts of web scripting languages like JavaScript and Python, progressing through the intricacies of consuming and creating RESTful APIs, and culminating in advanced topics such as secure API authentication, automation of data pipelines, and integration with machine learning workflows. It begins by demystifying web scripting - explaining how client-side and server-side scripting work, why they are crucial for data collection, and how data scientists can leverage them to streamline workflows. The core of the book focuses on APIs, which have become the backbone of modern data exchange. Readers will learn what APIs are, the different types (REST, SOAP, GraphQL), and practical methods to consume data using JavaScript and Python. Advanced chapters explore how to build your own REST API services, authenticate API calls securely, and handle real-time data streams. The book also emphasizes visualization and automation - teaching how to use web data effectively in dashboards and automate data collection for continuous, real-time insights. Finally, it presents two comprehensive project-based chapters, enabling readers to apply their knowledge to build functional dashboards and deploy APIs integrated with machine learning models. Who Should Read This Book? This book is ideal for: Data Science Students: Whether at undergraduate, postgraduate, or professional certification levels, students who want to expand their data science toolkit with practical web and API skills will find this book invaluable. It complements traditional data science curricula by adding a web-focused perspective critical for handling live and streaming data sources. Aspiring Data Scientists and Analysts: Professionals aiming to work in industries where data is sourced from multiple APIs, such as finance, healthcare, marketing, and social media analytics, will benefit by learning how to programmatically access, manipulate, and visualize data. Software Developers Transitioning to Data Science: Programmers with some coding background who want to upskill in data-centric applications will find the web scripting and API integration sections accessible and directly applicable. Researchers and Academics: Those who need to gather large datasets from web services or automate data retrieval for experiments and analysis will gain effective methods for scaling their data collection. Data Engineers and Machine Learning Practitioners: Professionals focused on building data pipelines and deploying machine learning models that consume live data from APIs will find practical guidance in creating APIs and automating workflows. Technical Enthusiasts and Hobbyists: Anyone interested in the intersection of web technologies and data science, looking to create data-driven web applications or personal projects.

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