When you can't find an existing data set that has what you need, then you can gather your own data. In this video, learn how to choose when creating your own data makes sense for your data science projects.
- [Instructor] Sometimes you need something special, … something that's not already there. … In the data science world, … there's a lot of data you can get from in house data, … open data APIs, and even data scraping, … but if you still can't get the data you need … to answer the questions you care about, … then you can go the DIY route, and get your own data. … There are several different ways to go about this. … The first one I would recommend is just natural observation. … See what people are doing. … Go outside, see what's happening in the real world, … or observe online, … see what people are saying about the topics … that you're interested in. … Just watching is going to be the first, … and most helpful way of gathering new data. … Once you've watched a little bit about what's happening, … you can try having 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, …
Author
Barton Poulson
Released
8/8/2019- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.
Skill Level Beginner
Duration
Views
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Introduction
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1. What Is Data Science?
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The data science pathway4m 51s
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2. The Place of Data Science in the Data Universe
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Artificial intelligence8m 22s
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Machine learning8m 6s
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Big data5m 36s
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Predictive analytics4m 57s
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Prescriptive analytics7m 42s
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Business intelligence4m 40s
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3. Ethics and Agency
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4. Sources of Data
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Data preparation5m 26s
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In-house data2m 6s
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Open data4m 49s
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APIs2m 40s
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Scraping data4m 44s
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Creating data5m 37s
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Self-generated data3m 30s
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5. Sources of Rules
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6. Tools for Data Science
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Languages for data science3m 55s
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7. Mathematics for Data Science
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Algebra7m 25s
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Calculus5m 3s
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Bayes' theorem4m 25s
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8. Analyses for Data Science
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Descriptive analyses6m 38s
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Predictive models7m 32s
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Trend analysis6m 22s
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Clustering5m 45s
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Classifying5m 34s
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Dimensionality reduction5m 42s
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Validating models4m 55s
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Aggregating models4m 8s
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9. Acting on Data Science
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Interpretability3m 17s
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Actionable insights2m 53s
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Conclusion
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Next steps2m 47s
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Video: Creating data