Week 4: Probability and Statistics in Data Science

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In Chapter 5 of the Data Toolkit, I read about Probability and Statistics for data science. These are fundamental concepts that are a massive part of data science. These concepts help data scientists understand and interpret data, make predictions and evaluate performance. 

Descriptive statistics are numerical and have value. Examples of these are mean, median, mode, range, variance and standard deviation. 

Probability distributions describe the likelihood of different outcomes for a variable. Some of these include the gaussian, binomial, and poison distributions. An example of this would be describing a natural  phenomena, such as someone's height.


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