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WGU D204 Data Analytics Journey Study Guide 2023 - 2024
1.Data preparation Time: data preparation 80%, and everything else
falls into about 20%
2.GIGO: garbage in, garbage out. That's a truism from computer
science. The information you're going to get from your analysis is only as good as the information that you put into it
3.Upside to In-house data: It's the fastest way to start., you may
actually be able to talk with the people who gathered the data in the first place.
4.Downside to In-house data: if it was an ad-hoc project, it may not be
well documented. And the biggest one is the data simply may not exist. Maybe what you need really isn't there in your organization. 1 / 4
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5.Open data: Basically it's data that is free because it has no cost and
it's free to use that you can integrate in your projects. Sources:
Number one is government data, number two is scientific data and the third one is data from social media and tech companies
6.APIs: An API or Application Programming Interface isn't a source of
data but rather it's a way of sharing data, it can take data from one application to another. Uses JSON files
7.Scraping data: Data scraping is, in a sense, the found art of data
science. It's when you take the data that's around you, tables on pages and graphs in newspapers, and integrate that information into your data science work. Unlike the data that's available with API's or Application Programming Interfaces, which is specifically designed for sharing, Data scraping is for data that isn't necessarily created with that integration in mind.
8.Scraping Data and Ethics: there's still legal and ethical constraints
that you need to be aware of. For instance, you need to respect people's privacy. If the data is private, you still need to maintain that privacy. You need to respect copyright. Just because something's on the web doesn't mean that you can use it for whatever you want. The idea here is Visible Doesn't Mean Open just like in an open market just 2 / 4
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because it's there in front of you and doesn't have a price tag doesn't mean it's free. There are still these important elements of laws, policies, social practices that need to be maintained to not get yourself in some very serious trouble. And so keep that in mind when you're doing Data scraping.
9.Creating data/Get your own Data: natural observation, informal
discussions with, for instance, potential clients. You can do this in person in a one on one, or a focus group setting. You can do it online through email, or through chat, and this time you're asking specific questions to get the information you need to focus 3 / 4
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your own projects.Surveys. Words > Numbers. Let ppl express themselves. Start general
10.Research Ethics when gathering data: informed consent,Also
sometimes confidentiality, or anonymity
11.Passive collection of training data: gathering enormous amounts of
data doesn't always involve enormous amounts of work. In certain respects, you can just sit there and wait for it to come to you. Photo
Classificaiton. issue with this:One, and this is actually a huge issue, is
that you need to ensure that you have adequate representation; things like categorizing photos/ limit cases
12.Self-generated data: external reinforcement learning.generative
adversarial networks. Internal
13.The enumeration of explicit rules: business strategies, flowcharts, Or
criteria for medical diagnoses.
14.expert system: An expert system is an approach to machine
decision-making in which algorithms are designed that mimic the
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