Syllabus

Data Science Basics and Data Scientist Toolbox:

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.

Developing Data Products and Working with NumPy:

Shiny, solidify, googleVis, and numPy.