This hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the “big themes” of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it’s the nuances within these larger themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional 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:

Ensure that your data science workflow creates value
Design actionable, timely, and relevant metrics
Deliver compelling narratives to gain stakeholder buy-in
Use simulation to ensure that your ML algorithm is the right tool for the problem
Identify, correct, and prevent data leakage
Understand incrementality by estimating causal effects

Daniel Vaughan has led data teams across different companies and sectors and is currently advising several fintech companies on how to ensure the success of their data, ML, and AI initiatives. Author of Analytical Skills for AI and Data Science (O’Reilly), he has more than 15 years of experience developing machine learning and more than eight years leading data science teams. Daniel holds a PhD in economics from NYU.

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data science the hard parts techniques for execelling at data Science daniel vaughan pdf download data science

Data science: The hard parts. Techniques for Execelling at Data Science by Daniel Vaughan (2023, O'Reilly)

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