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Throughout this unit, you used lots of different tools to help make decisions about which movie would be the most profitable.

When you first read the movie proposals, you used your own intuition and experience to form hypotheses about what makes a movie successful.

Those hypotheses helped you narrow down the list of movie ideas.

Then, you analyzed data about recent films to identify elements that make movies successful.

You graphed the data on histograms and scatter plots to help visualize patterns.

Then, you used spreadsheet formulas to analyze the data further.

Each member of your group created a movie poster to make your choice appealing to potential audiences.

And with your website, you conducted an A-B marketing test to collect additional data about the movie posters your group designed.

None of these analytic tools can make a decision for you -- or guarantee that your ideas will be successful.

But when you use them in combination, they can help you make better, more reliable decisions.

While many careers require the ability to analyze data, there are also careers that focus on it.

These careers often require some knowledge of programming.

For example, a data scientist works with huge amounts of data to generate insights for a company.

A data scientist might decide how to tune the recommendation software for a video streaming company, or how to use medical data to better treat patients at a hospital.

Even if you’re not a data scientist, you’ll use data to evaluate choices, compare and contrast options, and make better decisions in whatever career you choose.

Until next time, look for more ways you can apply data to everyday decisions!

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