In this video, learn how agency and accountability for decisions can be balanced between humans and data science algorithms in different ways that best suit the practical needs and legal environment.
- When we think about Artificial Intelligence … and how it works, and how it might make decisions, … and act on it's own, we tend to think of things like this. … You've got the robot holding the computer right next to you. … But the fact is, most of the time … when we're dealing with Artificial Intelligence, … it's something a lot closer to this. … Nevertheless, I want to suggest at least … four ways that working data science can contribute … to the interplay of human and Artificial Intelligence … of personal and machine agency. … The first is what I call simple Recommendations. … And then there's Human-in-the-Loop decision making. … Then Human-Accessible decisions, … and then Machine-Centric processing and action. … And I want to talk a little more about each of these. … Let's start with Recommendations. … This is where the algorithm processes your data … and makes a recommendation, … or suggestion to you and you can either take it or leave it. … A few places where this approach shows up are things …
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: Agency of algorithms and decision-makers