- Main
- Computers - Organization and Data Processing
- Data Science: The Hard Parts:...
Data Science: The Hard Parts: Techniques for Excelling at Data Science
Daniel Vaughan你有多喜欢这本书?
下载文件的质量如何?
下载该书,以评价其质量
下载文件的质量如何?
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).
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).
年:
2023
出版:
1
出版社:
O'Reilly Media
语言:
english
页:
257
ISBN 10:
1098146476
ISBN 13:
9781098146474
文件:
PDF, 8.35 MB
您的标签:
IPFS:
CID , CID Blake2b
english, 2023
在1-5分钟内,文件将被发送到您的电子邮件。
该文件将通过电报信使发送给您。 您最多可能需要 1-5 分钟才能收到它。
注意:确保您已将您的帐户链接到 Z-Library Telegram 机器人。
该文件将发送到您的 Kindle 帐户。 您最多可能需要 1-5 分钟才能收到它。
请注意:您需要验证要发送到Kindle的每本书。检查您的邮箱中是否有来自亚马逊Kindle的验证电子邮件。
正在转换
转换为 失败