The Well-Grounded Data Analyst: Solve messy data problems like a pro (Final Release)

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—andThe 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.

InThe 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
TheWell-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 hostDavid 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.

Foreword byReuven M. Lerner.

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 bookThe Well-Grounded Data Analyst introduces you to eight scenarios that every data analyst is bound to face. You'll practice authorDavid 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.

About the authorDavid Asboth is a data generalist educator, and software architect. He co-hosts the Half Stack Data Science podcast.

Table of Contents

1 Bridging the gap between data science training and the real world
2 Encoding geographies
3 Data modeling
4 Metrics
5 Unusual data sources
6 Categorical data
7 Categorical data: Advanced methods
8 Time series data: Data preparation
9 Time series data: Analysis
10 Rapid prototyping: Data analysis
11 Rapid prototyping: Creating the proof of concept
12 Iterating on someone else's work: Data preparation
13 Iterating on someone else's work: Customer segmentation
A Python installation instructions