I'm thinking about graduate school and have an interesting project at work. To help guide both, I have started a 12 week learning adventure with UC Berkeley Haas Data Science: Bridging Principles and Practice. Week 0 was assessment and refresh.
Statistics for Business:
Decision Making and Analysis,
3rd. Edition.
VARIABLES
categorical variable: Column of values in a data table that identifies cases with a common attribute. Sometimes called qualitative or nominal variables (no order, see ordinal). (Fruit, Types, zip codes, Identification Numbers, etc). these are continuous variables.
ordinal variable: A categorical variable whose labels have a natural order. (rating system 10 Best and 1 Worst).  A Likert scale is a measurement scale that produces ordinal data, typically with five to seven categories.  Another example can be Tiny, Small, Med, Large, Jumbo.
numerical variable: Column of values in a data table that records numerical properties of cases (also called continuous variable). (Amounts, dates, times)
measurement unit: Scale that defines the meaning of numerical data, such as weights measured in kilograms or purchases measured in dollars. CAUTION: The data that make up a numerical variable in a data table must share a common unit.
area principle:Â The area of a plot that shows data should be proportional to the amount of data.
time series: A sequence of data recorded over time.
timeplot:Â A graph of a time series showing the values in chronological order.
frequency: The time spacing of data recorded in a time series.
distribution:Â The collection of values of a variable and how often each occurs.Â
frequency table: A tabular summary that shows the distribution of a variable, count.
relative frequency: The frequency of a category divided by the number of cases; a proportion or percentage.Â