Types of Data
It’s been said that Data Scientist is the“sexiest job title of the 21st century.” Why is it such a demanded position these days?
The short answer is that over the last decade there’s been a massive explosion in both the data generated and retained by companies, as well as you and me. Sometimes we call this “big data,” and we’d like to analyze, extract patterns, draw conclusions, make predictions with the huge amount of data lying around.
Data scientists are the people who make sense out of all this data and figure out just what can be done with it.
What is Data?
Dictionary meaning of Data is “facts, such as numbers, words, measurements, observations and statistics collected together for reference or analysis.”
Data — Information — Statistics
— Data is measurement of some kind that you are collecting. This is, ‘raw unprocessed information’.
We typically, perform some statistical analysis on this data, and draw some meaningful conclusions out of the data.
Why does Data Matter?
- Helps in understanding more about the data by identifying relationships.
- Helps in predicting the future or forecast based on the previous trend of data.
- Helps in determining patterns that may exist between data.
- Helps in detecting fraud by uncovering anomalies in the data.
Data matters a lot nowadays as we can infer important information from it.
Most of the times, your data can be either ‘structured’ or ‘unstructured’.
Structured (organized) data: Data that can be broken down into observations and characteristics. They are generally organized using a tabular method (where rows are observations and columns are characteristics).
- Example: Meteorological data, as reported by scientific instruments in precise movements, would be considered highly structured as they exist in a tabular row/column structure.
Unstructured (unorganized) data: Data that exists as a free-flowing entity and does not follow standard organizational hierarchy such as tabularity.
When dealing with structured, tabular data (which we usually be doing), the first question we generally ask ourselves is whether the values are of a numeric or categorical nature.
Quantitative data is information about quantities; that is, information that can be measured and written down with numbers.
Qualitative data is information about qualities; information that can’t actually be measured. Some examples of qualitative data are the softness of your skin, the color of your eyes, etc.
Visualize Types of Data
- Categorical Data can be visualized by Frequency distribution, Bar Plot, Pie Chart, Pareto Chart.
- Numerical Data can be visualized by Histogram, Line Plot, Scatter Plot
Hope by now, you have got a good understanding of the types of Data.
In the next post, we will look at 4 measurement levels of data.