- [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.…
- 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
1. What Is Data Science?
2. Fields of Study
Ethical issues2m 39s
4. Data Sources
5. Data Exploration
8. Applied Statistics
9. Machine Learning
Next steps2m 17s
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