- [Voiceover] Once you've done exploratory graphics,…the next step is exploratory statistics…or numerical explorations of your data.…The general principle, again, is this:…First graphics, then numbers.…With exploratory statistics,…it's important to remember that you're still exploring;…you're not modeling the data.…Also, you tend to use empirical estimates,…or estimates of the population based on your sample data…as opposed to theoretical or analytical estimates.…You can also check the effects of manipulating the data,…and you can see how the data react to variation.…
Now, there are three general categories…of methods used in exploratory statistics.…They are Robust statistics,…resample the data or resampling statistics,…and transform the data or transformations.…Let's look at each of these in turn.…First, Robust statistics.…I have a mountain because they're not easily moved.…They're robust.…Robust statistics are stable in the presence of anomalies.…They're less affected by outliers, skewness,…kurtosis, and so on.…
- 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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