Data Pitfalls

Use of quantitative data, especially in financial markets, may provide rapid results due to the ease-of-use and availability of fast computational software, but this book advises caution and helps to understand and avoid potential pitfalls.

What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics.

Recent data shows that 87% of Artificial Intelligence/Big Data projects don't make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.

Unlock the Power of Statistical Analysis to Uncover Relationships and Make Informed Decisions