According to the Oxford “Data is distinct pieces of information, usually formatted in a special way”.
Data is measured, collected and reported, and analyzed, whereupon it is often visualized using graphs, images or other analysis tools. Raw data (“unprocessed data”) may be a collection of numbers or characters before it’s been “cleaned” and corrected by researchers. It must be corrected so that we can remove outliers, instrument or data entry errors. Data processing commonly occurs by stages, and therefore the “processed data” from one stage could also be considered the “raw data” of subsequent stage. Field data is data that’s collected in an uncontrolled “in situ” environment. Experimental data is the data that is generated within the observation of scientific investigations.
Data can be generated by:
- Human-Machine combines.
It can often generated anywhere where any information is generated and stored in structured or unstructured formats.
Why data is important ?
- Data helps in make better decisions.
- Data helps in solve problems by finding the reason for underperformance.
- Data helps one to evaluate the performance.
- Data helps one improve processes.
- Data helps one understand consumers and the market.
Types of Data:
Generally data can be classified into two parts:
- Categorial Data:
In categorical data we see the data which have a defined category, for example:
- Marital Status
- Political Party
- Eye colour
- Numerical Data:
Numerical data can further be classified into two categories:
- Discrete Data:
Discrete data contains the data which have discrete numerical values for example Number of Children, Defects per Hour etc.
- Continuous Data:
Continuous data contains the data which have continuous numerical values for example Weight, Voltage etc.
- Discrete Data:
At advanced level, we can further classify the data into four parts:
- Nominal Scale:
A nominal scale classifies data into several distinct categories in which no ranking criteria is implied. For example Gender, Marital Status.
- Ordinary Scale:
An ordinal scale classifies data into distinct categories during which ranking is implied For example:
- Faculty rank : Professor, Associate Professor, Assistant Professor
- Students grade : A, B, C, D.E.F
- Interval scale:
An interval scale may be an ordered scale during which the difference between measurements is a meaningful quantity but the measurements don’t have a true zero point. For example:
- Temperature in Fahrenheit and Celsius.
- Ratio scale:
A ratio scale may be an ordered scale during which the difference between the measurements is a meaningful quantity and therefore the measurements have a true zero point. Hence, we can perform arithmetic operations on real scale data. For example : Weight, Age, Salary etc.
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