In data science, as in life, it is important to focus on what matters most. In this video, learn how feature selection, or the choice of meaningful variables, is one way to do this in data science models.
- [Instructor] I teach statistics to undergraduate students … who don't always see how it connects to their lives. … I can give specific examples about each of the fields, … but I found that even the most recalcitrant student … can get excited about data … when we talk about sports like baseball. … Baseball's a data-friendly sport. … It's been going on for over 100 years, … there are 162 games in the regular season, … and they count everything. … If you're trying to figure out how good, for example, … a particular batter is, … you can start with these basic bits of data … and you'll have an enormous amount of information … to work with. … These are the features in the dataset that you start with. … But if you're a coach or a manager, … you can do a lot more … than just use those raw data points to make a strategy. … You can start combining them … to create new features in your dataset … and finding value and possibilities in your team. … Now you can start with really simple ones. … This is the batting average, …
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: Feature selection and creation