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

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).

這本實用指南提供了一系列在大多數資料工程和資料科學教育中常常被忽視的技巧和最佳實踐。一個常見的誤解是，優秀的資料科學家是這個領域的“大主題”——機器學習和編程的專家。但實際上，這些工具只能帶我們走到一定程度。在實踐中，真正區分出優秀的資料科學家和一般的資料科學家的是那些較小的工具和技能。

整本書的教訓將使一個普通的資料科學家候選人與一個在領域中有資格的資料科學家之間產生差異。作者Daniel Vaughan已經收集、擴展和應用這些技能，以創造價值並培訓來自不同公司和行業的資料科學家。

通過這本書，您將能夠：
- 瞭解資料科學如何創造價值
- 提供引人入勝的敘述以推銷您的資料科學項目
- 使用單位經濟學原則建立業務案例
- 使用故事講述方式為機器學習模型創建新功能
- 學習如何分解關鍵績效指標（KPI）
- 進行增長分解以找出指標變化的根本原因

Daniel Vaughan是墨西哥領先的支付技術公司Clip的資料主管。他是《Analytical Skills for AI and Data Science》（O'Reilly）的作者。