15 Questions
What is the CRISP data mining process?
A standard process for data mining
What is the purpose of 'decision analytic thinking' in data science?
To understand business problems more deeply
Why is it important to appreciate the long time required in data science?
To manage client expectations
What is included in the 'business understanding' phase of the CRISP data mining process?
Thinking about the problem to be solved and the use scenario
What is the main emphasis of 'data understanding' in the CRISP data mining process?
Assessing the strengths and limitations of historical data
Where can students find additional material for private study related to this lecture?
Minerva Learning Resources
In data science, when a stakeholder has a vague idea of the problem and no idea how to tackle it, what is expected from a successful data scientist?
To have a deep understanding of the stakeholder's business and terminology
What was the strategy adopted by Signet Bank in the 1990s to improve profitability?
Investing in acquiring necessary data and modeling
What did Signet Bank achieve as a result of its strategy in the 1990s?
It spun off credit card operations into Capital One
What was the aim of QuantiCode from 2016-2020?
To develop novel data mining and visualization tools and techniques
What is considered a successful method for solving a problem in data science when dealing with stakeholders who have a vague idea of the problem?
Preferably have face-to-face discussions, especially the first time
What kind of data preparation task involves converting & transforming data, data linkage, and addressing data leaks?
Data quality
'Decision analytic thinking' in data science is primarily focused on:
'Engineering' a solution to business problems
'Unsupervised & supervised tasks' are associated with which aspect of data science?
'Modelling'
'Clustering' and 'Co-occurrence grouping' are examples of tasks related to:
'Link prediction'
Test your knowledge on the business understanding aspects of data science based on the lecture and book 'Data Science for Business' by Foster Provost and Tom Fawcett. Content may include chapters 1, 2, and 11, as well as Appendix A.
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