Statistical Computing with R and Python

Course content

1. 1R and Python for statisticsComing
2. 2Data structures and data framesComing
3. 3Importing, cleaning and tidying dataComing
4. 4Data visualizationComing
5. 5Writing functionsComing
6. 6Random numbers and simulationComing
7. 7Monte Carlo methodsComing
8. 8The bootstrapComing
9. 9Numerical optimization for likelihoodsComing
10. 10Reproducible reports and version controlComing
11. 11Working with large dataComing
12. 12Capstone: an analysis pipeline from raw data to reportComing

Requirements

• STAT-151 Statistics for the Sciences
• CMPUT-174 Introduction to Computing Science

Recommended background. You can start any course at any time.

Description

Programming for data analysis: data wrangling, graphics, simulation, resampling and reproducible reports.

Part of the Science stream, typically taken in year 2.