week 6 Discussion

Yufei Sun 

Week 6

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Regarding to week 6 material, I am very interested in the topic of follow up decisions. As the professor said, we are constantly making decisions. we make 35,000 decisions a day (Stackexchange, 2018). We might make thousands of decisions to reach the final decision. In the process, there will be lots of problems come out, we have to make decisions to deal with them. For example, when we prepared to buy stuff, we have to consider if the price is suitable. And we have to decide on the brand that each brand has their own advantages and disadvantages. In addition, we might have to decide the size and where to place it. moreover, we might consider if we could save money to buy other stuff. To get the stuff we will face lots of decisions because there are lots of potential problems we have to consider. 

There are lots of benefits that follow-up decisions can bring to us. On of the benefit is the follow-up decisions will reduce the risk of the final decision. The more problems you come up with, the more decisions you will make, and then the less risk you will have. I believe each company does the same thing just like our project. When we get the final decision, there will be some follow-up decisions. Thus, it is necessary to resolve those problems that will make sure our final result nothing falls. 

This is our final week, I am very glad to have the same class with all you guys. Wish all you best. 

Thanks 

Reference 

Stackexchange. (2018). Basis for "we make 35,000 decisions a day" statistic. Psychology & Neuroscience Stack Exchange. Retrieved 15 August 2018, from https://psychology.stackexchange.com/questions/17182/basis-for-we-make-35-000-decisions-a-day-statisti

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Mengqiu Yao 

week 6 discussion

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Though Google Flu Trends project was a failure, we shouldn’t deny the potential contribution of big data to public health. The value of the data held by entities like Google is almost limitless, if used correctly (Lazer. D & Kennedy. R, 2018). The theory behind GFT is if there are large number of searches related to influenza in certain area in a period, then there’s a high possibility that there’s a corresponding influence population in the area. System will trigger early public health warning to relevant departments. The GFT failed because it overestimated the incidence of illness and sometimes doubled the data from CDC. The data is powered by Google’s rich search data in the flu season. The search results are highly related to the “self-estimated” flu symptom from the user population. Some people searched for related flu information because they were worried, and GFT may potentially marked these people positive with flu. However, Google may have potentially overestimated flu rate by doing so because they don’t have direct evidence to prove the illness. For example, searches for the COVID-19 may surged from February to June only because people want to get relevant information, but it doesn’t mean the COVID-19 cases increase sharply in an area at then. Google may have ignored the “noise” caused by these search results in the prediction model, thus affecting the accuracy of the model. Also, GFT over relied on statistical correlation, and directly replace it with “cause and effect” between things. I think the reason of the failure is that the analysts didn’t figure out the connection between keyboards searching and the spread of influenza, and they failed to find the reasons behind the connection. More and more cases have proven that data is not the more the better. No matter what, I think GFT is a very good attempt if data analysts can learn from it.  Reference:  Lazer, D., & Kennedy, R. (2018, June 06). What We Can Learn From the Epic Failure of Google Flu Trends. Retrieved August 11, 2020, from https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/

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