- [Voiceover] Data science is often practiced in teams.…It's a collaborative effort.…Consequently, what you need are several people…working on the project with complementary abilities.…Determine which skills are necessary for the project…and recruit people accordingly, or develop accordingly.…Let's go with a quick example.…Assume we have a hypothetical data scientist named Anthony,…and he's strong in data visualization.…He has good coding ability,…but he has limited analytics experience.…
And in fact, if we took the five characteristics…and we created a profile for Anthony…where he rated himself on a zero to ten…on each of these things, maybe it would like that.…And I've got a dotted line here at eight to say,…that's the level that you need…in order to do the project well.…Now he's past there on graphics, he's doing great,…but it's not everything that he needs…in order to do the project the way that it deserves.…Fortunately, there's another person.…Our other hypothetical person we'll call it Abby.…
She's got great business experience,…
- 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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