Statistics for Data Analysis Competency (Intermediate Level)

Topics covered

• analyze and visualize data using box plots
• calculate joint probabilities associated with the rolling of a die
• calculate marginal and conditional probability on dependent variables
• calculate the mean and median of a distribution using your own function and compare it with the built-in pandas function
• compute and visualize the standard deviation and variance of a distribution
• compute conditional probabilities
• compute marginal probabilities
• create a balanced sample using random undersampling and oversampling
• define and understand the Bayes theorem
• define joint, marginal, and conditional probability
• describe different types of probability distributions and where they occur
• enumerate the architecture of Bayesian networks
• explore the probability tables of nodes in a Bayesian network
• identify what different statistical terms represent
• implement simple random and stratified sampling on a data frame
• link the definitions of marginal and conditional probability
• outline the chain rule of probability
• query Bayesian networks to measure probabilities
• recognize how data is distributed using histograms and violin plots
• use pandas to generate a sample using cluster and systematic sampling