Overview. Some ideas for thinking about data.
Data analysis starts with a question. Generally, we want to learn something. In our world, we might ask
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What emerging market countries offer the best business environments?
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How is the US economy doing right now?
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How do returns on US and European stocks compare?
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How does average income vary across countries? Across states? Across zip codes?
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Where are our best customers?
You get the idea. The starting point is a question, something you'd like to know.
Once we have a question, we can start looking for data that might help us come up with an answer. We might ask ourselves: What data would be helpful in answering our question? Where can we find it? What should we do with it once we have it?
The question comes from you. What we'll provide in this course is a mentality for thinking about data and a toolset to work with it effectively.
It's not that we have no lives or anything, but we think about data all the time. If we read The Economist -- or the Wall Street Journal, or a blog post -- and see an interesting graphic, we look immediately at the source. Is it one we know? Can we get it ourselves?
Examples are all around us. Here are a few that caught our attention:
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FRED. Our go-to source for macroeconomic data. Note the "Notes" tab, it gives us the original source if we want to dig deeper.
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Gapminder world. Great interactive graphic. The data page gives sources.
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Market caps of tech firms. Interesting to see how quickly tech firms come and go.
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[Economic mobility by region](Inequality: http://www.nytimes.com/2013/07/22/business/in-climbing-income-ladder-location-matters.html). We love maps. This one shows that kids do better (relative to their parents) in some places than others.
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NBA shot charts. If you're into that kind of thing.