PROBABILITY AND STATISTICS WITH R
Probability and statistics are critical in data science. They allow us to gather insights from data and determine whether what we’re seeing is meaningful. This path introduces the basics of statistical analysis using R, including sampling, working with variables, and understanding frequency distribution tables.
SKILL PATH: PROBABILITY AND STATISTICS WITH R
Here's what you'll learn.
- How to summarize a distribution's measures of central tendency and variability
- Fundamentals of probability and how to use them for analysis
- How to create and test hypotheses using significance testing
Course Structure: Probability and Statistics with R
Learn about sampling data using simple random sampling, stratified sampling, and cluster sampling. Learn about building, visualizing, and comparing frequency distribution tables.
Summarizing a distribution using the mean, the weighted mean, the median, or the mode. Measuring the variability of a distribution using the variance and the standard deviation. Learning how to locate and compare values using z-scores.
Learn to assign probabilities based on conditions, probabilities based on event independence, and probabilities based on prior knowledge. Create spam filters using multinomial Naive Bayes.
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but how you learn it.
Learn by writing and validating code,
not by watching videos.
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