Intro to Understanding Data

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We are surrounded by data. Smartphones,

doctors, schools, fitness trackers,
governments, sports, researchers,

and web apps are creating mountains of
data. Big data. We often want to look for

patterns in the data to help us
analyze a variety of questions.

From the serious, like what are the causes
of heart disease, to the fun,

like who will be the next great football
player. For example, you might be

interested to know what patterns are
associated with higher pay at your job.

Well, no big surprise, but academic
achievement goes hand-in-hand with earning

lots of money. And studies have shown that
if you grow up in a house with lots of

books on the shelves, that you tend to do
better in school. So books on the shelves

cause you to do better at school which
leads to more pay. Easy enough.

Add books to your shelves and you'll make
a lot more money. But wait,

is it the number of books on your shelves
that actually causes better academic

performance? Is it possible that a higher
IQ of your parents would lead to both more

books on your shelves and better
academic achievement for you?

Looking at just books and academic
performance without considering your

parents' IQ would be a classic case of
what's called omitted-variable bias.

Or could we possibly be seeing what's
called reverse causation?

That is academic achievement causes more
books and not the other way around.

Don't worry. These terms sound confusing,
but they are not. Omitted-variable bias

sounds fancy, but it just means you left
an important factor. In this case,

your parents' IQ when studying academic
achievement. Understanding these terms and

more broadly understanding how to make
sense of data is a crucial skill in the

modern world. As data analysis is spilling
into almost every industry and phrases

like regression analysis, correlation coefficients,
and p-scores are showing up everywhere.

You're going to dive into understanding
data not through a typical lecture format

but through interactive play. You'll play
with some fascinating real-world data

sets, and through that exploration, learn
the intuition behind statistical analysis

and econometrics.

My name is Lorens Helmchen. I'm a
professor at the George Washington

University. Alongside my colleague Thomas
Stratmann from George Mason University,

we are thrilled to offer this free course
which is the first of its kind.

Click to get started playing with data.

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