book

The Well-Grounded Data Analyst

Name: The Well-Grounded Data Analyst
Author: David Asboth
ISBN: 9781633437531

by David Asboth

February 2025

Beginner to intermediate

472 pages

13h 7m

English

Manning Publications

Read now

Unlock full access

The Well-Grounded Data Analyst
copyright
dedication
contents
foreword
preface
acknowledgments
about this book
Who should read this bookHow this book is organized: A road mapAbout the codeliveBook discussion forum
about the author
about the cover illustration

1 Bridging the gap between data science training and the real world
1.1 The data analyst’s toolkit1.2 A results-driven approach1.2.1 Understand the problem1.2.2 Start at the end1.2.3 Identify additional resources1.2.4 Obtain the data1.2.5 Do the work1.2.6 Present the minimum viable answer1.2.7 Iterate if necessary1.3 Project structureSummary
2 Encoding geographies
2.1 Project 1: Identifying customer geographies2.1.1 Data dictionary2.2 An example solution: Finding London2.2.1 Setting ourselves up for success2.2.2 Creating the first iteration of a solution2.2.3 Review and future steps2.3 How to use the rest of the bookSummary
3 Data modeling
3.1 The importance of data modeling3.1.1 Common data modeling tasks3.2 Project 2: Who are your customers?3.2.1 Problem statement3.2.2 Data dictionary3.2.3 Desired outcomes3.2.4 Required tools3.3 Planning our approach to customer data modeling3.3.1 Applying the results-driven process to data modeling3.3.2 Questions to consider3.4 An example solution: Identifying customers from transactional data3.4.1 Developing an action plan3.4.2 Exploring, extracting, and combining multiple sources of data3.4.3 Applying entity resolution to deduplicate records3.4.4 Conclusions and recommendations3.5 Closing thoughts on data modeling3.5.1 Data modeling skills for any projectSummary
4 Metrics
4.1 The importance of well-defined metrics4.2 Project 3: Defining precise metrics for better decision making4.2.1 Problem statement4.2.2 Data dictionary4.2.3 Desired outcomes4.2.4 Required tools4.3 Applying the results-driven method to different metric definitions4.3.1 Questions to consider4.4 An example solution: Finding the best performing products4.4.1 Combining and exploring product data4.4.2 Calculating product-level metrics4.4.3 Finding the best products using our defined metrics4.5 Closing thoughts on metrics4.5.1 Skills for defining better metrics for any projectSummary
5 Unusual data sources
5.1 Identifying novel data sources5.1.1 Considerations for using new datasets5.2 Project 4: Analyzing film industry trends using PDF data5.2.1 Problem statement5.2.2 Data dictionary5.2.3 Desired outcomes5.2.4 Required tools5.3 Applying the results-driven method to extracting data from PDFs5.4 An example solution: Effects of the COVID-19 lockdown periods on the film industry5.4.1 Inspecting the available data5.4.2 Extracting data from PDFs5.4.3 Analyzing the data extracted from PDFs5.4.4 Project conclusions and recommendations5.5 Closing thoughts on exploring novel data sources5.5.1 Skills for exploring unusual data sources for any projectSummary
6 Categorical data
6.1 Working with categorical data6.1.1 Methods for handling categorical data6.1.2 Working with survey data6.2 Project 5: Analyzing a survey to understand developer attitudes toward AI tools6.2.1 Problem statement6.2.2 Data dictionary6.2.3 Desired outcomes6.2.4 Required tools6.3 Applying the results-driven method to analyzing the developer survey6.4 An example solution: How do developers use AI?6.4.1 Exploring categorical data6.4.2 Analyzing categorical survey data6.4.3 Project progress so farSummary
7 Categorical data: Advanced methods
7.1 Project 5 revisited: Analyzing survey data to determine developer attitudes to AI tools7.1.1 Data dictionary7.1.2 Desired outcomes7.1.3 Summary of the project so far7.2 Using advanced methods to answer further questions about categorical data7.2.1 Binning continuous values to discrete categories7.2.2 Using statistical tests for categorical data7.2.3 Answering a new question from start to finish7.2.4 Project results7.3 Closing thoughts on categorical data7.3.1 Skills for working with categorical data for any projectSummary
8 Time series data: Data preparation
8.1 Working with time series data8.1.1 The hidden depth of time series data8.1.2 How to work with time series data8.2 Project 6: Analyzing time series to improve cycling infrastructure8.2.1 Problem statement8.2.2 Data dictionary8.2.3 Desired outcomes8.2.4 Required tools8.3 Applying the results-driven method to analyzing road traffic data8.4 An example solution: Where should cycling infrastructure improvements be focused?8.4.1 Investigating available data and extracting time series8.4.2 Project progress so farSummary
9 Time series data: Analysis
9.1 Project 6 revisited: Analyzing time series to improve cycling infrastructure9.1.1 Problem statement9.1.2 Data dictionary9.1.3 Desired outcomes9.2 Where should cycling infrastructure improvements be focused?9.2.1 Analysis of time series data9.2.2 Project conclusions and recommendations9.3 Closing thoughts: Time series9.3.1 Skills for working with time series data for any projectSummary
10 Rapid prototyping: Data analysis
10.1 The rapid prototyping process10.1.1 Rapid prototyping example10.2 Project 7: Build a proof of concept to investigate Welsh property prices10.2.1 Problem statement10.2.2 Data dictionary10.2.3 Desired outcomes10.2.4 Required tools10.3 Applying the results-driven method to investigating Welsh property data10.4 An example solution: Building a prototype to explore using house price data10.4.1 Analyzing data before prototyping10.4.2 Investigating geographic aspects of a dataset10.4.3 Identifying how to present data in the prototype10.4.4 Project progress so farSummary
11 Rapid prototyping: Creating the proof of concept
11.1 Project 7 revisited: Building a proof of concept to investigate Welsh property prices11.1.1 Data dictionary11.1.2 Desired outcomes11.1.3 Project summary so far11.2 Building a proof of concept11.2.1 Preparing to build a proof of concept11.2.2 Using streamlit to build a proof of concept11.2.3 Project outcomes and next steps11.3 Closing thoughts on the rapid prototyping of ideas11.3.1 Skills for rapid prototyping for any projectSummary
12 Iterating on someone else’s work: Data preparation
12.1 Finding similar entities12.2 Continuing someone else’s work12.3 Project 8: Finding customer segments from mobile activity12.3.1 Problem statement12.3.2 Data dictionary12.3.3 Desired outcomes12.3.4 Required tools12.4 Applying the results-driven method to creating the second iteration of a customer segmentation12.5 An example solution: Creating customer segments12.5.1 Recreating someone else’s analysis12.5.2 Analyzing event data to learn about customer behavior12.5.3 Project progress so farSummary
13 Iterating on someone else’s work: Customer segmentation
13.1 Project 8 revisited: Finding customer segments from mobile activity13.1.1 Data dictionary13.1.2 Desired outcomes13.1.3 Project summary so far13.1.4 Segmentation of mobile users using clustering13.1.5 Conclusions and next steps13.2 Closing thoughts: Segmentation and clustering13.2.1 Skills learned to use for any projectSummary
appendix Python installation instructions
A.1 Installing PythonA.2 Installing poetryA.3 Creating your virtual environment
index

Content preview from The Well-Grounded Data Analyst

about the author

David Asboth is a “data generalist.” Currently, he works as a freelance data consultant and educator with an M.S. in Data Science, and has a background in software and web development in various industries. His previous roles include a range of data science, software development, and software architecting jobs, and his latest interest is figuring out what skills the analysts of the future actually need to succeed. Beyond having over a decade’s worth of technical experience in multiple industries, David cohosts the Half Stack Data Science podcast about data science in the real world, has spoken at multiple conferences, including ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781633437531Publisher Support Publisher Website

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

The Well-Grounded Data Analyst

by David Asboth

about the author

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.