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By layering the functions we’ve written, we can write coordinating functions
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And now, finally, we have something amazing.
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We can put all of the code we have written so far together.
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By layering the functions we have written, we can write coordinating functions,
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or little robots.
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Once we've written [SOUND] functions with instructions on how to do things,
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then we can create functions that execute [SOUND] those instructions.
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Thanks for watching.
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I hope you enjoyed this introduction to data science.
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Analytical thinking combined with hard data can help you convey your messages
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to your audience about what you're demonstrating.
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Labeling any figures to help guide your audience can avoid confusion.
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Python gives you wings, but remember to stay grounded.
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Try to break down your tasks by writing functions, and
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following the PEP 8's style guidelines, and being precise in your definitions.
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You'll thank yourself later.
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For example, say you wanna filter by brand name for one report, but in the next,
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you need to filter by material.
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By having one function that filters the string type fields,
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it allows you to pass in different arguments depending on your search
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criteria, which can mean that you avoid repeating yourself with the tasks, and
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just pass in the arguments for different strings.
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Everything we've covered in this course will be easier to code with the pandas
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library as it contains built-in functions.
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Now that you understand the concepts, you're ready to try out pandas and
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more advanced features of matplotlib.
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Now that you've finished this course, here's some projects you can try.
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You can download more recent data from ShopSense.
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You can create more reports with the updated data and
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you can compare the reports from two different time periods
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to see if the tie prices have changed.
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Go out, have fun, and enjoy the fruits of your labor.
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