- [Voiceover] Creating data.…There are simply times when the data isn't available…and you need to make your own.…Now, when talking about the various methods…that can be used,…there are a few factors that distinguish them.…For instance, there's the level of intervention.…Is it observational, which is strictly hands off,…or is it experimental,…where you get in the mix and you create a situation?…What kind of data type do you have?…Is it quantitative, or numerical, data,…which is what we often use in data science?…Or is it qualitative, specifically text,…which can be analyzed but requires different approaches?…And how about the location?…Is it online or is it in person?…All of these combinations can be used to gather data…that you need, if you're careful about your design.…
So, for instance, let's look at one, interviews.…These are ideal for new topics or audiences.…They can be structured or unstructured.…However, interviews are generally very time-consuming,…and they may require a very specialized training.…This is one of the situations where you actually may…
- 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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