- [Voiceover] Algebra is a key practice in data science.…Algebra is the study of abstract quantities…and the relationships between them.…There are three kinds of algebra that are particularly…relevant to data science.…There is elementary algebra, that's the kind we all…learned, that's using calculating individual values,…you multiply this, you add that.…There is linear algebra also known as matrix algebra.…This is at the core most of the calculations of statistical…procedures, and then there is systems of linear equations.…
These are critical to linear algebra and also to the…practice of optimization which we'll take a closer look at.…Right now I'm gonna do an example on data science salaries,…and this actually comes from real data.…I'm gonna make a particular equation that goes…salary is equal to some constant plus years,…this actually has to do with age, plus bargaining…plus hours plus an error term.…You could write it like this with abbreviations,…but it's more common to write it like this.…
Let me explain the terms that are here.…
Author
Released
7/27/2016- 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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Welcome58s
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Exercise files34s
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1. What Is Data Science?
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Ethical issues2m 39s
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4. Data Sources
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Existing data4m 36s
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APIs4m 38s
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R5m 18s
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Python4m 51s
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SQL3m 44s
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Web formats3m 53s
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7. Mathematics
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Algebra6m 22s
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Systems of equations5m 11s
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Calculus9m 50s
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Big O5m 8s
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Bayes probability8m 15s
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8. Applied Statistics
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Hypothesis6m 23s
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Confidence5m 42s
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Problems5m 30s
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Validating3m 35s
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9. Machine Learning
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k-nearest neighbors (kNN)5m 26s
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Naive Bayes classifiers5m 16s
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Artificial neural networks5m 43s
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10. Communicating
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Interpretability5m 50s
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Actionable insights4m 40s
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Reproducible research3m 27s
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Conclusion
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Next steps2m 17s
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Video: Algebra