Overview

Complete eight data science projects that lock in important real-world skills—along with a practical process you can use to learn any new technique quickly and efficiently. Data analysts need to be problem solvers—and  The Well-Grounded Data Analyst will teach you how to solve the most common problems you'll face in industry. You'll explore eight scenarios that your class or bootcamp won't have covered, so you can accomplish what your boss is asking for. In  The Well-Grounded Data Analyst you'll learn: High-value skills to tackle specific analytical problems Deconstructing problems for faster, practical solutions Data modeling, PDF data extraction, and categorical data manipulation Handling vague metrics, deciphering inherited projects, and defining customer records The Well-Grounded Data Analyst is for junior and early-career data analysts looking to supplement their foundational data skills with real-world problem solving. As you explore each project, you'll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host  David Asboth. You'll learn how to determine a minimum viable answer for your stakeholders, identify and obtain the data you need to deliver, and reliably present and iterate on your findings. The book can be read cover-to-cover or opened to the chapter most relevant to your current challenges.   About the technology: Real world data analysis is messy. Success requires tackling challenges like unreliable data sources, ambiguous requests, and incompatible formats—often with limited guidance. This book goes beyond the clean, structured examples you see in classrooms and bootcamps, offering a step-by-step framework you can use to confidently solve any data analysis problem like a pro.

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9781633437531

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David Asboth is a ""data generalist"", with experience as a freelance consultant, educator, software architect, and more. He co-hosts the Half Stack Data Science podcast about data science in the real world, has spoken at multiple conferences including the Data Science Festival in London, and has taught a variety of data science courses to enterprise students including large banks and consultancies.

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