WEBVTT - generated by Mediathek

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Hi, my name is Willie Hernandez and I'm going
to be your professor in this part of the module.

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In this part of the module, we'll witness how
math provides us

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with an understanding of the world of data.

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We'll begin with continuous random variable
probability distributions —

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The foundations of measuring uncertainty.

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This is not a theory: this is the mathematics
of how AI systems

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and business make decisions every day.

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We will then move on to distribution tests
and contingency tables.

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These enable us to infer if patterns within
data

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are real or just random noise.

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Think of a company like Bolt

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testing if reconfigurations of their app layout
actually increase user experience —

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these methods make such decisions reliable.

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Then we have correlation and regression analysis.

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Correlation shows us whether two things tend
to move together,

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and regression allow us to make predictions
about outcomes.

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For example, a website like GetYourGuide could
use regression

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to understand how price and reviews influence
bookings.

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And finally, we will learn to assess regression
models.

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That is, not just building a model,but also
thinking critically about

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whether it truly depicts reality or it might
deceive us.

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Such a skill is essential for everybody who
operates with data in the era of AI.

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By mastering these principles, you will have
a solid basis to understand

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how data drives companies, strategies, customer
experience, and innovation.

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If you can read the math that underlines the
data,

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you are prepared to lead in today's accelerated
world.