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  • Data Science: The Hard Parts: Techniques for Excelling at Data Science

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Data Science: The Hard Parts: Techniques for Excelling at Data Science

by Daniel Vaughan (Author) Format: Paperback
4.3 out of 5 stars (9)

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This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you will:

  • Understand how data science creates value
  • Deliver compelling narratives to sell your data science project
  • Build a business case using unit economics principles
  • Create new features for a ML model using storytelling
  • Learn how to decompose KPIs
  • Perform growth decompositions to find root causes for changes in a metric

    Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

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  1. ISBN-10
    1098146476
  2. ISBN-13
    978-1098146474
  3. Edition
    1st
  4. Publisher
    O'Reilly Media
  5. Publication date
    December 5, 2023
  6. Language
    English
  7. Dimensions
    7 x 0.5 x 9 inches
  8. Print length
    254 pages
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From the Publisher

Data Science: The Hard Parts: Techniques for Excelling at Data Science

From the Preface

I’ll posit that learning and practicing data science is hard. It is hard because you are expected to be a great programmer who not only knows the intricacies of data structures and their computational complexity but is also well versed in Python and SQL. Statistics and the latest machine learning predictive techniques ought to be a second language to you, and naturally you need to be able to apply all of these to solve actual business problems that may arise. But the job is also hard because you have to be a great communicator who tells compelling stories to nontechnical stakeholders who may not be used to making decisions in a data-driven way.

So let’s be honest: it’s almost self-evident that the theory and practice of data science is hard. And any book that aims at covering the hard parts of data science is either encyclopedic and exhaustive, or must go through a preselection process that filters out some topics.

I must acknowledge at the outset that this is a selection of topics that I consider the hard parts to learn in data science, and that this label is subjective by nature. To make it less so, I’ll pose that it’s not that they’re harder to learn because of their complexity, but rather that at this point in time, the profession has put a low enough weight on these as entry topics to have a career in data science. So in practice, they are harder to learn because it’s hard to find material on them.

The data science curriculum usually emphasizes learning programming and machine learning, what I call the big themes in data science. Almost everything else is expected to be learned on the job, and unfortunately, it really matters if you’re lucky enough to find a mentor where you land your first or second job. Large tech companies are great because they have an equally large talent density, so many of these somewhat underground topics become part of local company subcultures, unavailable to many practitioners.

This book is about techniques that will help you become a more productive data scientist. I’ve divided it into two parts: Part I treats topics in data analytics and on the softer side of data science, and Part II is all about machine learning (ML).

While it can be read in any order without creating major friction, there are instances of chapters that make references to previous chapters; most of the time you can skip the reference, and the material will remain clear and self-explanatory. References are mostly used to provide a sense of unity across seemingly independent topics.

This book is intended for data scientists of all levels and seniority. To make the most of the book, it’s better if you have some medium-to-advanced knowledge of machine learning algorithms, as I don’t spend any time introducing linear regression, classification and regression trees, or ensemble learners, such as random forests or gradient boosting machines.

Editorial Reviews

From the Author

After giving a series of technical, analytics, and non-technical talks to my teams of data practitioners (data engineers, BI analysts, data analysts, and data scientists) at my previous company, I came to the realization that:
  1. For several of these topics, there isn't much literature to refer to for further reading, other than a few scattered blog posts.
  2. The material could be of interest to a wider audience who are asking the same questions or making the same mistakes.
  3. There was enough material to write a book.
The problem was that the material was all over the place, from the highly technical nuts and bolts of machine learning and model productization, to the softer side of storytelling in data science. Nonetheless, I decided to move forward and partner again with O'Reilly to create this book (back in 2020, I published "Analytical Skills for AI and Data Science" with them).
From my last writing endeavor, I was worried about the personal toll experienced by having a full-time job during the day and a writing job at nights and over the weekends. But compared to Analytical Skills, this book was written very fluidly. It could be that writing a second book is easier, or that my mental state was in a better place. In any case, writing The Hard Parts was a fun and fluid experience.
Both books have a large underlying theme: I'm obsessed with making better decisions with data and evidence, and I want to share what I've learned with others. In a previous stage of my life, I had dreams of becoming an academic, so "sharing" is intrinsic to that facet of my life. But sharing also forces you to ensure that your understanding is thorough, and to admit with humility when you're wrong about something.
But while Analytical Skills goes into detail about the methods for data-driven decision-making, The Hard Parts use this ideal as the North Star and presents techniques that won't affect the quality of the decisions made directly. Rather, these are subsidiary techniques that will make you a better data scientist.
I'm deeply satisfied with the end product, even if sometimes I felt that I gave insufficient space to a topic (but I'm expanding on many of these on my Substack channel). I'm now looking forward to the next writing project. All I can say is that the North Star is the same.
I hope the book is useful to you.

About the Author

Daniel Vaughan is currently the Head of Data at Clip, the leading paytech company in Mexico. He is the author of Analytical Skills for AI and Data Science (O'Reilly, 2020). With more than 15 years of experience developing machine learning and more than eight years leading data science teams, he is passionate about finding ways to create value through data and data science and in developing young talent. He holds a PhD in economics from NYU (2011). In his free time he enjoys running, walking his dogs around Mexico City, reading, and playing music.

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ December 5, 2023
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 254 pages
  • ISBN-10 ‏ : ‎ 1098146476
  • ISBN-13 ‏ : ‎ 978-1098146474
  • Item Weight ‏ : ‎ 2.31 pounds
  • Dimensions ‏ : ‎ 7 x 0.5 x 9 inches
  • Best Sellers Rank: #744,407 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.3 out of 5 stars (9)

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