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Preparing data for analysis is vital for working with and translating your data.
Welcome to the last stage of this course.
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Nice work.
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We've covered a lot of material.
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So let's go over it one more
time before you move on.
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We've learned the basics
of data cleaning and
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ways to make sure that our
data is correct and accurate.
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If you feel like cleaning your
data set takes a long time,
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remember that it's normal.
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Properly cleaning your data
takes a lot of work, but
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it's vital to the quality
of your analysis.
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We covered eight types of data errors or
problems.
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Formatting errors, incorrect data type,
nonsensical data entries,
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duplicate entries, missing data,
saturated data, systematic and
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individual errors, and
confidential information.
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You should have an idea of how to start
cleaning up these problems when you see
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them in your data.
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We also talked about how important
it is to understand your dataset.
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A lot of errors can go unnoticed if you're
not sure what your data should look like,
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or what's normal.
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It's also important when deciding which
data should be kept in your dataset, and
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what should be removed.
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And since we’re using Python,
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we talked about ways we can automate parts
of our data analysis to help it go faster.
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That’s all for this course, but
there’s always something new to learn.
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In the teacher’s notes, you can
download marked down commented versions
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of the code we used
throughout this course.
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You can use these to remind yourself
what we’ve covered in the lessons.
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There are also links to resources for
learning more about data cleaning and
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data analysis.
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Now there is one more quiz and
then you are done with this course.
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Congratulations and good luck.
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