This course will focus on practical coding for research using Python and R. We will begin discussing the versioning tool Git, the command line, and downloading tools to begin coding with these languages. Then we will talk about how to use Jupyter notebooks and responsibly use AI to help with the coding process. Next, we will begin reading, writing, and analyzing data and images with Python. We will then shift to using R and RStudio. Beginning with how to organize a project, load and manage R packages, and set up the AI Copilot tool within RStudio. This will lead into data types and structures and data cleaning. We will then focus on analyzing these data with univariate and multivariate statistical methods. Finally, we will show how to visualize data within Python and R.
| Month | Savings |
|---|---|
| S01_20240822_python_session: | what is Python and installing Python. |
| S02_20240829_python_session: | installing Python environment (miniconda, VScode, jupyter notebooks). |
| S03_20240905_python_session: | basic Python variables types and functions. |
| S04_20240912_python_session: | basic Python data manipulation with Pandas, image analysis, and univariate statistics. |
| S05_20240919_r_basics_1: | R and RStudio and how to use them. Loading packages. Basic 1D and 2D data types and manipulation. |
| S06_20240926_r_data_cleaning_2: | cleaning data using R. Using data cleaning packages like dplyr, janitor, ... |
| S07_20241003_r_data_cleaning_3: | long versus wide data. Combining data sets |
| S08_20241017_r_statistics_viz_4: | base R stat functions and plotting. Basics of ggplot2. |