From the course: Data Science Foundations: Fundamentals
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In-house data
From the course: Data Science Foundations: Fundamentals
In-house data
- [Instructor] Data science projects can feel like massive overwhelming undertakings like epic expeditions. But sometimes you can get started right here, right now. That is your organization may already have the data that you need. And it may be for instance, the fastest way to start because it's already in the format that you need. Also, restrictions may not apply. A lot of the things about like GDPR, and FERPA and privacy regulations. Well, if the data's being used exclusively within the organization that gathered it for their own purposes, maybe some of those regulations don't apply, which means you have a little more flexibility in what you're able to do. Also, maybe you can talk with the creators, maybe the people who gather the data in the first place are still there, and you can get some of the details you need about the process. And so between getting up and running right away, maybe having a little more latitude in how you work with the data, and the ability to talk with the…
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Contents
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Data preparation5m 26s
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(Locked)
Labeling data for supervised learning8m 48s
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(Locked)
In-house data5m 38s
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(Locked)
Open data4m 15s
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(Locked)
APIs2m 39s
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(Locked)
Scraping data4m 45s
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(Locked)
Synthetic data and simulation environments7m 12s
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(Locked)
Passive collection of training data3m 57s
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(Locked)
Data vendors5m 30s
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(Locked)
New data from surveys and experiments5m 36s
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(Locked)
Data ethics5m 14s
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