- [Voiceover] A topic that can be easily overlooked…in data science analyses, unfortunately,…is that of validating models.…The question here is, Are you on target with your analysis?…The problem is that many machine learning algorithms…fail on implementation, many scientific studies…cannot be replicated.…And so there's the question of whether your analysis…is actually giving you good insights…into the general nature of the problem…and not just the specific data at hand.…To put it another way, your model…fits the sample data beautifully.…
It's tailored, it's great, but will it fit other data?…This is considered the issue of generalizability…or scalability.…Now, one way of looking at this…is with posterior probabilities, where you take information…about your present data and you combine it…with information about the past to get…some sort of impression about the future.…Most analyses give you the probability…of the data given the hypothesis.…Fine, that's the basis of standard testing,…but there's more interest and more utility…
- 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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