Please use this background information to answer your questions in this section: Three MBA students/classmates in Bayes Business School had different working experiences: one student worked in a large investment bank based in London as a junior financial analyst previously, one student worked in a small FinTech company that focuses on Big Data Machine Learning and the other student was a data scientist in a giant Tech company. During their MBA final course project with a real-word case study, they realise that many companies in London struggle to engage the best diverse talents, while people from less advantaged backgrounds do not receive many career opportunities. As a result, after graduating from the MBA, these three classmates want to build a new business that will help connecting/matching these firms in London and individuals from minority ethnic backgrounds (or from poor families), in order to solve this specific talent search and matching problem.

Q no 1

Based on the content and knowledge that you have learnt in the data visualisation session, please discuss the following question: After three months of solid research and data analytics about the new business idea, from both qualitative and quantitative data, how do you plan to present your data/output/conclusion to these three MBA students, in order for helping them to make meaningful decisions?

Q no 2)

Explain what are common biases in data collection (both qualitative and quantitative), please list at least 5 examples (without linking to the background information). Based on your own judgement and supportive argument, please discuss three of these common biases as you listed that are potentially most relevant to your project in the above Question 1 and explain how to avoid them.