1. Interpret and produce various graphical displays of data and information and learn how to choose the most appropriate technique in a variety of situations.
2. Understand that all data has variability: separate that variability into information (knowledge) and error (unknown structure, noise, randomness)
3. Interpret and compute confidence intervals and data statistics (mean, median, histograms and significant differences).
4. Understand and perform basic probability calculations
5. Solve problems with statistical variables that have a binomial, Poisson, normal or other probability distributions
6. Fit and interpret linear correlations and regression and determine correlation coefficients between different data variables.
7. Use multiple regression to predict a response variable and determine the most significant predictor variables.
8. Design an experimental program and then interpret experimental data.
9. Use R and Python to process and analyze data.