Broadly data is categorized into three types, namely
- Numerical – Quantitative
- Categorical – Nominal
- Time Series
Numerical – Quantitative
Data are numbers and are not a function of any other quantity, such as time. Data that holds a proportional value of its representation.
Numerical data can be of two types.
The data can hold complete values as in integers such as 10, 25, 47, 82 …etc. It is considered as discrete. Let’s say a cricketer, a batsman can score only a whole number of runs such as 23, 65, 89 ..etc. He/She can not score 43.53 runs. Any type of data which can hold integer values is considered discrete. E.g., Whole numbers ( 2, 5, 6, 23, 534, …etc.)
The data which can hold floating-point values, i.e., continuous values, is termed as continuous data. The batting average of a cricketing batsman can be 43.0 or 37.32 …etc. Such type of data which can hold floating values is termed as continuous. E.g., Any value in a range (2.0, 4.23, 53.244, …etc.), the weight of a person.
Categorical – Nominal
Data that represents characteristics, for example, the color of a fruit, group of a candidate, the position of a cricket fielder, team, …etc. Categorical data can hold numbers, but they are representational. They don’t have meaning in a mathematical sense. For, e.g., one can not perform mathematical operations on categorical data. They just represent a characteristic. Although there is something called ‘ordinal data.’
Ordinal data is ordered categorical data. The best example of ordinal data grades, e.g., A, B-, B, C, C-, D, F these are representational letters for a group which your score falls in, but they do have order in which we know B is better than C- (c minus) and F is failing.
In simple words, time-series data is a sample of data collected at regular intervals of time, e.g., daily recording of the stock index (share market) value for over a year. Time series data is a data collection that is a function of time.
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