In limited domains, you can create enumerated lists of explicit rules for help algorithms solve common problems. Before investing in this approach, learn if it is likely to be effective for you in this video.
- [Instructor] Let's say you meet someone at a party … and after talking for awhile you start to wonder … if that person might be interested in you. … This is apparently a question … that is on a lot of people's minds. … If Google's auto-complete is to be trusted, … assessing attraction is a major research question. … Of the top 10 statements to start how to tell, … the first two are on this topic. … Shortly followed by how to tell an egg is bad … and in fact, men appear to be sufficiently difficult to read … that they get to appear twice on the top 10 list. … And so that let's you know we need an answer … to this question, how can you tell … if somebody is interested in you? … Well maybe we can propose some rules. … Maybe they're interested in you because they said so. … Or maybe they smiled and made eye contact. … Or maybe they just swiped right. … And again, there are the insecure doubts … that pop up and undermine your belief in these things. … Maybe it's wonderful to meet you isn't diagnostic …
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: The enumeration of explicit rules