Basics of data sciences and toolbox, the workflow of CLI and git, big data analysis, and experimental design.
Data Analysis with Python:
Pandas, time deltas, python plotting, data structures, and computational tools.
Getting Data:
Raw data, processed data, tidy data, web reading, API, data summarization and merging, regular expressions, and text variables.
Data Analysis and Research:
Graphical devices and plotting systems, basics of reproducible research, clustering, exploratory graphs, and basics of literate statistical programming.
Statistical Inference and Regression Models:
Probability and statistics, basics of statistical inference, regression models, distributions and likelihood, binary and count outcomes, and residual variations.
Machine Learning:
Caret, prediction with motivation, regression, and model and cross-validation.