PROBABILITY AND STATISTICS WITH PYTHON
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 Python, including sampling, working with variables, and understanding frequency distribution tables.
SKILL PATH: PROBABILITY AND STATISTICS WITH PYTHON
Here's what you'll learn to do.
- 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 Python
Sampling data using simple random sampling, stratified sampling, and cluster sampling, understanding what variables are in statistics, and how they're measured, and 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, assign probabilities based on event independence, assign probabilities based on prior knowledge, and create spam filters using multinomial Naive Bayes.
Learn how to perform a permutation test, perform significance testing to better understand an outcome's importance, and about regular and multi-category chi-square tests.
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