Data: From Raw to Ready
Ask anyone who analyzes data for a living where their time goes, and the answer is cleaning. This course is about that work: the export with three header rows, the age recorded as 512, the survey stored so awkwardly that no software can read it.
It starts with the idea that saves the most grief later, tidy data: one row per observation, one column per variable. From there it builds the habits that keep a project trustworthy. A codebook tells future-you what "grp" meant, validation rules catch the impossible value at entry, and a cleaning workflow never edits the raw file by hand. The middle lessons handle the recurring puzzles (outliers, recoding, reshaping, merging), and the course closes on reproducibility and privacy.
You don't need a coding background, though the principles map straight onto R or Python when you're ready. The course pairs well with Methods before it and Writing after it.