Predictive models use many variables to predict likely scores or outcomes on a case-by-case basis. In this video, learn how the most common approaches, such as linear regression, are surprisingly flexible and powerful.
- [Instructor] Marriage is a beautiful thing … where people come together and set out on a new path … full of hope and possibilities. … Then again, it's been suggested that half of the marriages … in the U.S. end up in divorce, … which is a huge challenge for everyone involved. … But 50 percent's just a flip of a coin. … If you were trying to predict whether a particular marriage … would last or whether it would end in divorce, … you could just predict that everybody would stay married … or maybe everybody would get divorced … and you'd be right 50% of the time without even trying. … In a lot of fields, being right 50% of the time … would be an astounding success. … For example, maybe only 5% of companies that receive … venture capital funding end up performing as projected … and there's billions of dollars at stake. … If you could be right 50% of the time … in your venture capital investments, you'd be on fire. … And that brings up the obvious question: … How can you tell which companies will succeed …
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: Predictive models