The legal, ethical, and social environment for data science has evolved dramatically over the last few years. In this video, learn about recent developments to help you avoid missteps and build better relationships with your customers.
- [Instructor] Data science can make you feel … like a superhero … who's here to save the world … or at least your business's world. … But an alarming amount of data work … can also end up in court … or on the wrong side of a protest … so I want to talk about a few things … that can help keep you, your company … and your data science work on the up and up. … First, there are some important legal issues. … Now it used to be when data science first came about, … you know, oh, 10 years ago, … we were kind of in the Wild West … and people just kind of did what they wanted … but now we've had some major developments … in the legal frameworks that govern data and its use. … Probably the most important of these … is an entire collection of privacy laws, … the most significant of which at the moment … is the GDPR, that's the European Union's … General Data Protection Regulation. … This is a law about privacy … that has some very serious teeth. … It can potentially have fines of billions … of dollars for companies …
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: Legal, ethical, and social issues of data science