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  • - I think one of the good new applications

  • of data science is in the medical field.

  • Like in drug delivery or cancer treatment.

  • - I think a very interesting one

  • is how now companies can use all the information

  • they're gathering from their customers

  • to actually develop new products

  • that respond to the needs

  • of the customers.

  • - A good new application of data science

  • was the high trending news of Pokémon Go.

  • So they used Ingress.

  • They used data of the Ingress app.

  • The last app of the same company

  • and they choose the locations

  • for Pokémons and gyms

  • according to data from the last app.

  • So they learned with their errors.

  • - Google Search is an application of data science.

  • The Google Search, whenever we want to search anything.

  • So I think its all because of data science.

  • Whatever Google is now, it's all because of data science.

  • - Augmented reality is my favorite

  • new implementation of data science.

  • I think you can't look at a new technology

  • and not see data science in there

  • but augmented reality is the one

  • I'm just the most excited about.

  • The ability to walk around and see things on walls

  • or around us that aren't really there.

  • Pokémon's just the start.

  • - So what has happened is that now the tools are available

  • and datasets are available,

  • people are applying them with not much diligence

  • and I think one of the strange cases

  • which got reported in the newspapers is about the story

  • of a father walking into a Target store in the US

  • and complaining about the fact

  • that the Target was sending mails to his teenage daughter

  • about diapers and milk, baby formula.

  • He was angry with them.

  • He said, "Why would you like

  • "for my teenage daughter to have a baby?"

  • And he was obviously disturbed

  • by this mail or the ad campaign.

  • And they obviously apologized

  • but then the father returned two weeks later

  • and he apologized to them

  • saying he didn't know his daughter was pregnant.

  • Now the question is, how did Target know this thing

  • before the father knew.

  • And what has happened is that they would look

  • at the purchasing behavior of individuals.

  • So if you're buying some sort of supplements or vitamins

  • then you know that this is the first trimester of pregnancy.

  • So they know what products to send to you

  • assuming that the person

  • who bought those supplements were pregnant.

  • Now this is a great story about data science

  • and how data science can forecast and predict

  • these consumer behaviors

  • even before the family would find out.

  • And I find it disturbing and strange and odd

  • for a variety of reasons.

  • First of all, for every correct prediction,

  • you have hundreds of incorrect predictions

  • which we call the false positives

  • and no data scientist actually advertises

  • his or her false positives.

  • We only advertise and promote what we got it right.

  • But when we got it wrong hundreds of times we don't tell it.

  • Second thing is, that's an abuse of data.

  • That's basically not really not giving you much insight.

  • You've just found a correlation

  • but someone could be purchasing the same material

  • for someone else.

  • So, and then the odds of getting it wrong

  • and the odds of getting false positives is much higher.

  • So I find it strange and I think it gives a false sense

  • of our ability to predict the future.

  • The reality is about data science

  • and the most important thing

  • for the budding data scientist to know

  • that all forecasts are wrong.

  • They're useful but they're wrong.

  • And so one should not put their faith

  • into the fact that now that we can do predictive analytics

  • that we can solve all problems.

  • I think a good example is the Google Search.

  • Google published a paper saying

  • they can predict flu epidemics

  • before the Center for Disease Control.

  • And what they did was they were looking

  • at what people were searching on Google so flu symptoms.

  • So Google saw the flu symptom searches

  • before anybody else and they were able to predict it.

  • The thing is these searches are good

  • and they are correlated with some outcomes

  • but not necessarily all the time.

  • So at that time, when Google announced,

  • it was a big thing and everybody really like it

  • and well that's a new era of predictive analytics.

  • Only that a few years later they realized

  • that Google started to predict false positives.

  • That they were predicting things that were not really there

  • or the predictions were not that accurate

  • for a variety or reasons.

  • They changed probably their algorithms

  • and the datasets were not really correlated

  • with the outcomes.

  • So what's the lesson to learn here?

  • One has to avoid what we call the data hubris.

  • That you should not believe in your models too much

  • because they can lead you astray.

  • Data science has tremendous potential to bring change

  • in parts of the world, in parts of our society

  • that have been disenfranchised for years.

  • One sees great examples of data science

  • especially in the developing countries

  • where they are targeting relief efforts.

  • They're targeting food

  • and other aid to individuals,

  • to places that have not been targeted in the past.

  • And the reason it is happening now

  • is the greater availability of data and models and analytics

  • to be able to pinpoint where the greatest needs are.

  • The ability to design and conduct experiments

  • to see if one were to give micro-credits,

  • small loans to very poor households

  • in developing parts of the world,

  • to see how they affect

  • the individual household's ability to get out a poverty

  • and also the local community's ability

  • to collectively improve their economic well-being

  • by just very small infusions of cash or credit.

  • So these experiments happening all over the world

  • are allowing that is a direct result

  • of our ability to analyze data

  • and be able to design experiments

  • and then roll out humongous efforts

  • in providing relief, providing credit,

  • providing an opportunity

  • to those who have been disenfranchised in the past

  • an opportunity to join the rest of the world

  • in prosperity and happiness and health.

- I think one of the good new applications

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B1 中級

數據科學的應用[數據科學101] (Applications of data science [Data Science 101])

  • 102 11
    陳賢原 發佈於 2021 年 01 月 14 日
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