APIs, or application programming interfaces, allow you to connect your programs to external data sets. In this video, learn how to choose relevant APIs to save yourself time on programming and even data processing.
- [Narrator] When You draw a picture or write a letter, … chances are that you can draw well with one of your hands, … your dominant hand and not so much with the other. … I recently heard someone describe this as … having a well developed API for your dominant hand … but only a clunky one for the non-dominant hand. … 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 … or from a server to your computer. … It's the thing that routes the data, translates it, … and gets it ready for use. … I want to show you a simple example of how this works. … So I've gone to this website that has what's called … the JSON Placeholder. … JSON stands for JavaScript Object Notation, … it's a data format and if we scroll down here, … you'll see this little, tiny piece of code … and what it says is go to this web address … and get the data there … and then show it, include it and you can just click on this …
Author
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
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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: APIs