Algorithms that play complex games have successfully developed winning strategies simply by being taught the rules of the games. Learn when this approach is most powerful in data science in this video.
- [Narrator] When I was growing up, … I remember an ad for toys that said … wind it up and watch it go. … But now you can do a similar kind of thing … with data science. … You can do this looping back process. … This is where computers, the algorithms in them, … can engage themselves to create the data they need … for machine learning algorithms. … It's a little bit like the mythical self-consuming snake … that comes all the way back around. … And the reason this is important is because you need data … for training your machine learning algorithms … so they can determine how to categorize something … or the best way to proceed … and having the machines generate it … by engaging with themselves is an amazingly efficient … and useful way of doing that. … There are at least three different versions of this … and I'm giving a little bit of my own terminology here. … The first one is what I am calling … external reinforcement learning. … Now reinforcement learning is a very common term. … It means an algorithm that is designed …
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: Self-generated data