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- Published Year: 2013
- Page count: 177
- File Size: 16 MB
- Language: English
- Published by: O'Reilly Media
- Visited by: 110
- Rating/Review: 4.0
- ISBN: 978-1-44932-626-5
کانال تلگرام امین مصباحی
تِکافترنون، رویدادی گاهبهگاه است با موضوعات حول معماری و توسعه نرمافزار، این کانال هم برای اشتراک اخبار، آموزش، نکاتی حول مهندسی نرمافزار، دیتابیس، تکنولوژی و مدیریت تولید محصولات نرمافزاری خواهد بود. امین مصباحی
Learn MoreAgile Data Science
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Each download or ask from book AI costs 2 points. To earn more free points, please visit the Points Guide Page and complete some valuable actions.Introduction to Agile Data Science
Welcome to Agile Data Science, a guide to connecting the worlds of data and business with a modern, dynamic, and iterative approach. In a world driven by data, organizations must adapt quickly, iterate often, and build meaningful data products to succeed. This book equips individuals, teams, and organizations with a proven methodology to leverage data for impactful, measurable outcomes. Whether you're a data scientist, data engineer, or product manager, this book will help you harness agile practices to create reliable, effective, and innovative solutions.
Detailed Summary
At its core, Agile Data Science merges the principles of agile software development with the tools and processes essential for modern data science. Data science today is not just about analyzing data; it’s about creating valuable and actionable insights, products, and visualizations that solve real-world problems. Traditional approaches to data science often fail to deliver timely results in high-impact business contexts. This is where agile methodology becomes essential.
The book takes you on a step-by-step journey of building successful data-driven products through iterative exploration and development. It begins by introducing the necessary agile principles and processes tailored for data science. It then dives into the tools and workflows that drive these processes, including Python, machine learning models, web-based visualization tools, and API architectures. No matter where your project starts — from data wrangling to model deployment — you'll learn how to execute efficiently using lean experimentation cycles.
The book brings practical insights through real-world case studies and examples, allowing you to connect its teachings to your work. By following this guide, you will be equipped to deliver faster iterations, collaborate effectively across teams, and reduce the time-to-insight for your data products, all while maintaining strong communication with stakeholders.
Key Takeaways
- Understand and apply agile principles specifically adapted for data science workflows.
- Learn to create a Minimum Viable Product (MVP) for data-driven applications.
- Streamline data pipelines for faster development and actionable results.
- Master key tools for data visualization, machine learning, and API integration.
- Foster interdisciplinary collaboration between technical teams and decision-makers.
Famous Quotes from the Book
"Data is at its most powerful when it transforms raw information into a tool for decision-making."
"Agile data science is not about perfection; it's about quickly uncovering insights that drive action."
"The secret to successful data products is empathy — understanding the context and needs of the end user."
Why This Book Matters
In today’s rapidly changing business landscape, data-driven decision-making is paramount. Yet, many teams and organizations struggle to convert raw data into actionable insights. Traditional, siloed approaches to data science are too slow to keep up with the dynamic demands of modern businesses. Agile Data Science bridges this gap by showing how agility and iterative methodologies can transform the way data products are created and delivered.
By combining technological know-how with a people-first perspective, the book empowers teams to move beyond the complexities of large datasets, complex pipelines, and abstract algorithms. Instead, it focuses on delivering effective and timely solutions, ensuring your data science efforts are always aligned with business goals. As more businesses transition to data-first strategies, learning to think and work in an agile way becomes an essential skill. This book is your guide to achieving excellence in this fast-evolving domain.
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