Distinguish a dataset from a single fact, a list, or a file. Recognize a dataset as an organized collection of related data that can be read, described, and analyzed for a specific purpose.
Check what you understood with a short quiz.
Use what you learned in the previous lesson to solve real-world problems.
Read a table by matching each row to an observation or record, each column to a variable, and each cell to a value. Practice saying what one row and one column mean in a small dataset.
Identify the exact thing each row represents, such as one person, one order, one day, or one measurement. Reason through how changing the unit of analysis changes what the variables mean.
Use column names and labels to understand what a variable represents, then write a short data dictionary entry with the variable name, plain-language meaning, and example values.
Classify variables as quantitative, categorical, Boolean, date-time, or text. Decide what kinds of comparisons or summaries make sense based on the values a variable can hold.
Recognize identifier variables such as customer_id, student_id, or product_code. Tell the difference between an identifier that names a record and a variable meant to describe or measure it.
Separate input variables from the outcome variable for a prediction question. Recognize features as the information used to make a prediction and the target, or label, as the value being predicted.
Turn a small dataset into clear plain-language statements: what the dataset is about, what each row represents, and what each column records. Practice spotting vague descriptions that do not say enough to interpret the data.
Review this chapter with practice based on your mistakes.
