- [Voiceover] Interpretability should…constantly be on your mind anytime…you're conducting a data science project.…Think of it this way.…You want to be a data-driven storyteller.…Or put it another way, you're trying to solve for value.…You're trying to find value in the data.…The problem is that the analysis on its own…does not constitute value.…Instead, I like to think of it this way.…Analysis times the story or the narrative…that you give it, that's equal to value.…Now note, this relationship is multiplicative.…
It's not additive, and if you remember about multiplication,…if you have zero story, then you're gonna have…zero value when you're done with the project.…So your goal should be to maximize the story…or the narrative or the interpretability of your project…to maximize the value you get out of your data…and out of your analysis.…Think about your goals.…The analysis is goal-driven, should be.…The story that you tell, the narrative that you give,…should match those goals.…Among other things, answer any questions…
- 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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