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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.
Solve Any Data Analysis Problem guides you through eight common scenarios you'll encounter as a data scientist or analyst. 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.
In Solve Any Data Analysis Problem 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
In Solve Any Data Analysis Problem you’ll learn how to shift the way you think about data from the structured clean problems you get in a classroom, book, or bootcamp to the messy open-ended challenges of the workplace. As you work through eight problems you’ll see over and over on the job, you’ll discover a solutions-driven methodology that’s focused on getting results. 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.
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 book
Solve Any Data Analysis Problem presents eight industry scenarios that you’re sure to encounter in your Data Science career. The book can be read cover-to-cover, or opened up to whichever chapter is most relevant to your current challenges. You’ll explore data modeling by identifying customer records from retail transactions, navigate badly defined metrics, and learn how to extract data from PDFs, etc.
Get your hands dirty with categorical data that defies conventional statistical analysis, and unearth hidden patterns in seemingly simple time series data. Plus, you’ll learn what to do when you’re handed someone else’s work to expand on, and how to wow your stakeholders with your presentations. By the time you’re done reading and working, you’ll have the kind of on-the-job skills it normally takes years to learn—and an amazing portfolio of projects to show off at an interview.
Bridging the gap between data science training and the real world
Project 1: Encoding geographies
Project 2: Data modeling
Project 3: Metrics
Project 4: Unusual data sources
Categorical data
Categorical data: Advanced methods
Time series data: Data preparation
Time series data: Analysis
Rapid prototyping: Data analysis
Rapid prototyping: Creating the proof of concept
Iterating on someone else’s work: Data preparation
Iterating on someone else’s work: Customer segmentation0
Appendix. Python installation instructions