Standard statistical approaches can yield data-based rules for decision-making such as regression coefficients. In this video, learn how to depending on your goals, this may be a quick and effective approach.
- [Narrator] A dancer has to spend years … working on their craft to deliver a masterful performance. … One of the paradoxes of this training is that sometimes … you have to think a little bit less in order to move better. … Your conscious processes can interfere … with fluid and meaningful movement. … Sometimes you just have to calm down all your ideas … about expert decision-making systems … and the rules that they bring along, … and let the data have a say in … how you should go about your work. … We'll start by looking at linear regression, … which is a common and powerful technique … for combining many variables in an equation … to predict a single outcome, … the same way that many different streams … can all combine into a single river. … We'll do this by looking at an example … based on a data science salary survey. … So this is based on real data, … and the coefficients are based on the actual analysis, … although I'm only showing a few of the variables … that went into the equation. …
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: The derivation of rules from data analysis