And why is that? Just lore.

You say, This photograph, it's about it depicts this person.

Νo vaccine data, no nothing.

But first thing, when you see dots like this, you have to ask yourself from where do the data come?

Is this proposition true? Is this theory a good theory?

The best answer is to run scatterplots, and the reason is I taught you that you should always look at the data first before doing anything with the data.

Trade has increased.

No one had asked this question in his community before.

But almost no malaria experts even know it exists.

it's relevant data.

So there's some complex relationships going on just on, just on three variables.

The height is guaranteed to be small so the operations are guaranteed to be fast.

What she was able to do was because she had access to the raw data.

And if you look here, and this is really interesting.

Well, 18 2 makes 36. Square root of this is 6. 6 6 is 1.

This is a supplemental system to the localization class I just taught you.

And this graph doesn't even mean anything it's fake data.

Suppose we have 2 feature values.

You see our vehicle following another vehicle.

Now I, of course, thought,

The mean is obviously 25.

But it's not about questions. It's about data.

And here's the resulting data that comes from that, the model that comes from that.

It's an endless debate and there's no conclusive, single answer to it.

And the idea of now creating implants, scanning data, an MRI scan of somebody can now be converted into 3D data and we can create very specific implants for them.

And this is really just the sum of all the numbers divided by this is a human constructed definition that we've found useful the sum of all these numbers divided by the number of numbers we have.

The data was completely useless.

Taking the ratio of these two things gives us our answer of 1.004.

We have 1000 for x and 1000 for y.

Now, fortunately there are methods to search efficiently.

(Applause)

What makes these clusters so different is not the absolute location of each data point, but the connectedness of these data points.

We will now begin our journey into the world of statistics, which is really a way to understand or get our head around data.

Let's say our data set and I'll order it for us let's say our data set was 0, 7, 50, I don't know, 10,000, and 1 million.

These are the corner points in our data points.

That is where the tuna spawn.

Here's one on understanding genomics.

I teach global health.

They're representing some type of quantity.

And your intuition would be right.

Our data source decides whether it's reliable data, we don't.

My solution, I admit, is not the most elegant, but it's the simplest that I could find without going into more complicated data structures.

We will see an Einstein in Africa in this century.

This data is anything but accurate.

This data isn't accurate at all.