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.