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EVENT DETAILS |
This course is an introduction to the basic statistical principles often used by data scientists & applied statisticians including:
Common statistical issues & how to avoid fallacies.
High-level overview of probability & common statistical estimates.
Advanced topics like multiple hypothesis testing, independence, sample size & power calculations, & bootstrapping.
Statistical programming language R, one of the most popular languages for data science.
Who the course is designed for:
You are a numbers person & math-lover who wants to hone in on statistics skills in order to apply them to data science projects. You wish to prioritize accuracy & avoid fallacies while building towards data science success.
Outcomes
An understanding of basic statistical hypothesis testing & confidence intervals.
The ability to model data using well known statistical distributions, as well as handle data that is both continuous & categorical.
The ability to perform linear regression & adjust for multiple hypothesis.
An understanding of how to calculate the number of samples needed to achieve required sensitivity & specificity.
An understanding of bootstrapping & Monte Carlo simulation.
What you'll receive upon completion:
Certificate of completion
Certificate link & instructions on how to add to your LinkedIn profile
3.3 Continuing Education Units
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