![]() That is, the data is in the desired format/shape to begin the desired analysis immediately. Clean datasetĪ prepared and clean dataset is easier to use out of the gate because it is smaller and requires no data cleaning or data wrangling on the student’s part. ![]() ![]() Both options involve students using Python, R, Stata, SAS, Excel, or some other software to analyze (or clean then analyze) the data. Below we explain what we mean by clean and raw, as well as the circumstances that call for each. There are two high-level options of datasets: (1) a prepared, clean dataset and (2) a real-world, raw dataset. Courses we believe are a good fit for our data include those that focus on probability, statistics, econometrics, economics, federal budgeting, and data science. Quantitative courses at every level require datasets for classroom lessons, homework, problem sets, exams, and more.
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