How all data has uncertainty; why statistics is a powerful tool for reaching insights and solving problems; summarizing data using certain concepts; common statistical notation and graphing techniques; previewing the programming language R.

Dipping into the R programming language; the advantages of R over spreadsheets; walking through the installation of R; installing RStudio, a companion IDE; how to download specialized data packages from within RStudio.

Studying sampling and probability; how sampling aims for genuine randomness in the gathering of data; how probability provides the tools for calculating the likelihood of a given event; solving a range of problems involving probability.

Delving deeper into probability by surveying several discrete probability distributions; exploring some examples, each of which is tailored to answer a specific question; analyzing several sets of data using these tools.

Focusing on the normal distribution; characterized by a bell-shaped curve that is symmetrical around the mean, it shows up in a wide range of phenomena; using R to find properties connected to this ubiquitous data pattern.

How to measure covariance, which is the association between two random variables; using covariance to obtain a dimensionless number called the correlation coefficient; plotting correlation values for several variables using an R data set.