What is DATA? Types of Data in statistics
As technology continues to advance machine learning is becoming incredibly popular and effective. But, without data, machine learning is as useless as mobile without battery. It is the data that gives machine learning its power. And, as we are realizing this, the value of data is increasing day by day, and so is the need to learn and implement statistics to better understand and interpret the data
What actually does the DATA mean?
According to Wikipedia, data is a collection of discrete values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted.
Data can be seen as the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion
The word “data” was first used to mean “transmissible and storable computer information” in 1946.
Why do we need Data?
Without data, we would just make wild guesses and jump to the wrong conclusion. Data plays an important role in helping us make informed decisions and understand the world around us.
Data allows us to identify trends, patterns, and relationships that would otherwise be invisible and difficult to understand.
In short, data is essential to make correct decisions, conduct research, test hypotheses, and validate theories.
As we understand what DATA is and why we need it, let’s understand where and how we get IT.
Collecting Data
Collecting data can be a complex and time-consuming process. There are many different ways of collecting data that can be used depending on the factors like type of data needed and the scope of research. These data collection techniques can be divided into two types.
- Primary Data Collection: Process of collecting data directly from the resource or original research methods.
e.g. surveys, interviews, observations, and experiments. - Secondary Data Collection: Process of gathering data from existing sources.
e.g. government reports, academic studies, and business reports.
Data is useless until it is analyzed and understood. Understanding the characteristics and properties of data is crucial to know the nature of the data.
Types of DATA
By dividing data into different types, we can better understand its nature and derive meaningful insights from it.
Numerical Data/Quantitative Data
Numerical Data represents numbers or numerical values. It gives quantitative information about a specific thing like how much or how many. Numerical data can be divided into two types based on the nature of the data.
- Continuous Data: Continuous data is a type of numerical data that can be measured and take on any value within a given specific range, with an infinite number of possible values between any two values. Continuous data can be represented as a range of values instead of specific individual values.
e.g. Height, Weight, Temperature. - Discrete Data: Discrete data is a kind of numerical data that consists of discrete individual values. These values can be counted and shown using whole numbers or integers. Unlike continuous data, discrete data can only have a finite number of possible values, and they can’t be divided into smaller parts that are meaningful.
e.g. Number of students in a class, Sales happened in one year, Age.
Categorical Data/Qualitative Data
Categorical Data is also known as Qualitative Data because it describes the qualities or characteristics of the subject. Categorical data mostly consists of textual information which represents the features or nature of the subject.
- Nominal Data: Nominal Data represents the type of qualitative information that can not be ordered or ranked. Nominal data can be grouped into categories.
e.g. Gender, Hair color, Country. - Ordinal Data: Ordinal Data represents the type of qualitative information that follows a natural order or ranking. It is often used to represent subjective ratings or opinions.
e.g. Customer ratings(1–5), Education levels(school, college, graduate).
