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Data is Everywhere
3:13Data is all around us! In this video, we'll see where we can find data and talk about who uses data inside an organization.
To create an analysis,
first we need to find some data.
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Luckily, it's all around us.
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From how many steps you take in a day,
to how much gas is left in the car.
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Data is there helping
inform our decision making.
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Data is a record of an event,
or an attribute of something.
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You have the power to create data just by
recording the things happening around you.
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For example, every morning you could
write down how many hours you slept.
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That's data.
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A collection of data is
usually called a dataset.
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Generally, this means that all the data
in the set is related in some way.
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It may come from a common source,
or relate to one specific topic.
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For example, a dataset of the nutritional
information of vegetables
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is unlikely to include bananas.
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We saw in the grocery store example
that we all use data naturally.
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This happens in many of
our daily decisions.
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We might look at school rankings or
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neighborhood crime rates when
choosing a place to live.
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Read reviews of online products
before buying them, or
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compare features on different
appliances before choosing one.
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When we use information to make
a decision, no matter how large or
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small, we're using data.
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Data is also useful for many hobbies.
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If you're a sports fan, digging into
the stats of your favorite players,
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can be a great way to get
started working with data.
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Other interests may not be as
datacentric as sports are, but
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you can still approach them
from an analytic angle.
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For example, if you're a gardener,
gathering data about your local weather
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and how it affects different plants can
help you to choose the best plants to
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grow for your climate and give you the
greatest chance of success as a gardener.
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Or maybe you're just interested in data.
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I'm sure there's a dataset out
there that you'd find interesting,
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and there are tons of websites devoted
to sharing data with the public.
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A few you might want to look into are
Caggle, data.gov, and the UCI repository.
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Check out the information in the teacher's
notes below to get started in exploring.
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In the workplace,
data analysis is a growing field.
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More and more companies are creating
data teams to help them make decisions.
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People in many different
positions work with data.
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In job titles the word analyst
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often indicates that the position
involves data analysis.
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However, other titles are possible.
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For example, data scientists combine
data analysis with programming and
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machine learning.
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Different analysts might work with
different types of data within a company.
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For example, a web analyst might make
recommendations for a company's website
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based on which pages are visited most,
and which links are clicked most often.
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On the other hand, a financial
analyst would look at revenue and
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expenses to forecast a company's
financial performance.
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And a pricing analyst might use purchasing
data to determine the optimal prices for
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products.
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Since data is an emerging field,
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the same title in different
companies may mean different things.
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If you are looking for a job working with
the data, always read the job descriptions
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to make sure that the position aligns
with your interests and capabilities.
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In the next video, we'll introduce
the dataset we'll be working with and
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start doing some analysis
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