Book (Practical) Mathematics Collection of data analysis failure cases

Product description ※Please note that product information is not in full comprehensive meaning because of the machine translation.

Mathematics
[Introduction to Contents]
[Nightmare of Data Scientists]
- Projects whose superiors just want to say "I am using AI"
- Clients who only accept the results they expected
- Data that is not available even at the end of the project
- Analysts who are only interested in analytical methods
- Specifications that are finally decided by the final after report
[The analysis may have already failed. How to Avoid Failure and Get Closer to Success]
[The analysis may have already failed. How to Avoid Failure and Get Closer to Success]
This book contains 25 cases that have been reconstructed based on the "failure" of data analysis projects.
From these realistic cases, people involved in the use of data can learn about what they should not do to avoid failure.
From these cases, we can learn about what they should not do to avoid failure. We can learn from the critical factors that foretell the failure of a project. Failure is the mother of success!
CONTENTS
Work for Part I "Aiai"!
CASE1 is a must-read Personnel Affairs of Data Scientists
Part III Go Beyond That Failure
BI Report on CASE18 Chaos
CASE19 We only prepared aggregate data. How accurate was the prediction.
I was surprised to get CASE20, but I don't have enough usable data.
CASE21 We worked hard to predict.
CASE22 Seeing trees, not seeing forests, Kiken.
CASE23 Encounter with more than ten times the number of IDs of the total population
After the final report of CASE24 is completed, aggregate specifications are decided
Life of CASE25 Machine Learning Module
Column Never Fail Data Analysis
Conclusion product-oriented