- [Voiceover] Let's take a look…at some of the common problems in modeling,…that is when you're trying to build statistical models.…The most significant…is that data science can get complicated.…For instance, you might have non-normality in your data.…Most approaches like bell curves,…and if you have something else, you got a problem.…You might have nonlinearity…in the association between variables.…Straight lines are easier to deal with,…curves and other shapes are more difficult.…You might have multicollinearity,…which I've mentioned previously.…This is when your predictors are associated with each other,…there's overlap in their predictions…with the outcome variable.…
And you might have missing data,…which introduces a host of its own issues.…Let's look at each one separately.…Non-normality is a problem because…most approaches like bell curves.…Here's a nice, normal bell curve distribution.…But you might have a strongly skewed distribution.…Or you might have micked distributions…that are bimodal or have other shapes.…
- 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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