Название: Data Science: The Hard Parts: Techniques for Excelling at Data ScienceАвтор: Daniel Vaughan
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 257
Язык: английский
Формат: True PDF, True EPUB
Размер: 10.1 MB, 10.2 MB
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.
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.
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
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