Название: The Well-Grounded Data Analyst: Solve messy data problems like a proАвтор: David Asboth
Издательство: Manning Publications
Год: 2025
Страниц: 742
Язык: английский
Формат: epub (true)
Размер: 69.7 MB
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.
Which tool you are comfortable doing the above in does not matter. I will provide example solutions to the projects in the book using Python, but the focus will be on problem-solving, not the specifics of the Python programming language. Appendix A gives you a quick overview of the skills and tools in the basic data science toolkit. If you need to brush up on anything, we’ve linked to some useful resources you can use to get up to speed. As for most solutions I provide, the code itself will be written in Python, primarily using the Pandas library. While code snippets will be used to explain the example solution, I will focus discussions on the conceptual solution and less on the specifics of the code. The solution will be in three parts: setting up the problem statement and the data, creating the first iteration of a solution, and a third part to review the work and decide on further steps.
Completing the projects in this book will build on your foundational skill set by adding skills that are specific to real-world use cases. These include data modeling, working with categorical data, extracting data from unusual sources, and rapid prototyping. In each case, I will highlight the exact kind of functionality that is required to solve the problem so that you can find the appropriate way to do that with your preferred tool.
The future of data analysis will also include Artificial Intelligence (AI) tools, such as ChatGPT and similar Large Language Models. In the book, I will highlight instances where such a tool could help solve a part of your problem.
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.
About the Book:
The Well-Grounded Data Analyst introduces you to eight scenarios that every data analyst is bound to face. You’ll practice author David Asboth’s results-oriented approach as you model data by identifying customer records, navigate poorly-defined metrics, extract data from PDFs, and much more! It also teaches you how to take over incomplete projects and create rapid prototypes with real data. Along the way, you’ll build an impressive portfolio of projects you can showcase at your next interview.
What's Inside:
Deconstructing problems
Handling vague metrics
Data modeling
Categorical data manipulation
About the reader:
For early-career data scientists.
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