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Preparing Data for Analysis

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Context

1:59 with Megan Amendola

Determine the context necessary for working with our dataset.

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    Understanding the context behind your analysis may affect how you clean 0:00
    the data. 0:05
    Here are a few questions to ask yourself when reviewing the dataset. 0:06
    [MUSIC] 0:10
    Do you know enough about the topic of the dataset? 0:13
    Why are you working with this dataset? 0:17
    How do you plan on using the dataset? 0:19
    What questions do you need to answer? 0:23
    Who will be viewing the analysis? 0:25
    For instance, when sharing analysis with people from the US, you'll probably wanna 0:30
    have numbers in a format they're familiar with like inches or pounds. 0:35
    If you're presenting the information to people from really anywhere else in 0:39
    the world, it might make sense to share the numbers in centimeters or kilograms. 0:44
    Understanding the context also means knowing enough about the topic to be able 0:49
    to make accurate predictions and assumptions about the data. 0:54
    Let's use the Pokemon dataset as an example, 0:58
    you can find a link to the dataset in the teacher's notes below. 1:01
    If I were going to use this dataset for analysis but 1:05
    I didn't know anything about Pokemon, I would start looking for 1:08
    resources to help me understand what Pokemon are. 1:12
    What does a weakness or type mean and how do they interact with one another? 1:15
    Understanding the terminology of the dataset and how different elements relate 1:21
    to each other will also help you clean the dataset properly and 1:26
    start building out questions for your analysis. 1:29
    With this dataset for example, once I understand how weaknesses and 1:33
    types work, I could answer questions like, 1:37
    what type of Pokemon has the most weaknesses on average? 1:40
    Or which weakness shows up the most? 1:44
    Once you feel you have enough context to move on you can start looking for 1:48
    what needs to be cleaned. 1:52
    Let's take a look at the different types of bad data to look out for 1:54
    in the next video. 1:57

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