An Ultimate Guide to Types of Data

Various types of data play a crucial role in data science, statistics and market research. Even in programming languages, data types define the different functions to be performed while coding. This blog describes in detail multiple data types and the differences between them.

Types of Data

Data is broadly divided into two main categories based on the collection source: qualitative and quantitative data. 

Let us understand the difference between the two:

• The data collected in the form of numbers is numerical or quantitative data. On the other hand, the data that cannot be expressed in numbers is called qualitative data. It is described as information or description about items or people. 
• While quantitative data can be measured, qualitative cannot be measured and has a descriptive language- for example, colors and emotions. 
• The purpose of quantitative data is to count how many. On the contrary, qualitative data measures how and why.
• Researchers collect quantitative data by counting or measuring, whereas one needs to observe and note down the observations to collect qualitative data.
• The quantitative data remains the same in similar circumstances. However, qualitative data is subjective. It varies from person to person and circumstances.
• Quantitative data is collected through tools, questionnaires, surveys, and data analytics tools. It might be second-hand information as it is collected from different sources. On the other hand, qualitative data is generally fresh or firsthand because the researcher gathers it through their observations.

Qualitative and Quantitative data are further divided into two sub-categories each. Qualitative data is subdivided into nominal and ordinal data. Quantitative is further categorized into ratio and interval data. 

1. Nominal Data

The word ‘nominal’ is derived from Nomenclature, which means name. Therefore, nominal data is the name of a collection of variable data divided into various groups. In nominal data, the elements are not arranged orderly but separated based on a characteristic or trait. The labels or names of the group under which variable data is stored are called nominal data. It is not measured or calculated – elements are simply divided into various groups. Nominal data is also called categorical data. 

Following are the examples of nominal data:

• Colors (grey, black, and white)
• Clothing (skirt, jeans, and dresses)

2. Ordinal Data

The name ordinal is derived from the term order. Therefore, we can understand that ordinal data refers to information that is placed in an order. There is a hierarchical pattern in ordinal data.

Here are some examples of ordinal data:

• Customer satisfaction response (good, very good, average, bad, and very bad.)
• Exam grades (A, B, C, D, E)

Difference between nominal and ordinal data:

• Ordinal data is arranged in a hierarchical pattern, whereas nominal data is organized based on a particular characteristic or trait.

• Nominal data is collected by asking open-ended questions or multiple-choice questions. On the other hand, ordinal data is collected through rating scales.

3. Interval Data

Also called integer data, interval data comprises information like numbers that can be measured or arranged on a scale. As the name suggests, the elements in interval data are arranged at regular intervals. The space between any two elements in the interval data is always uniform. We can add or subtract interval data values. However, the values cannot be multiplied or divided. There is no starting or zero point in interval data. It is widely used in market research due to its uniformity.

• Organizations use SWOT analysis in interval data to find out their strengths and weaknesses.

• Interval data is used in trend analysis to research market insights across specific intervals.

4. Ratio Data

There is a lot of similarity between interval data and ratio data. The elements in a ratio data are also arranged on a scale with the uniform distance between two values. However, the difference in ratio data is that the two values are in proportion. There is a ratio between the two values. It is used to predict consumer sales or customer behavior.

Differences between interval and ratio data:

• There is no zero point in interval data, so there can be a negative value on the scale. However, in ratio data, there is zero value. Therefore, there can be no negative value in ratio data.
• In interval data, arithmetic mean is calculated, whereas in ratio data, geometric mean is calculated.

Types of Data in C Language

The variables in the C programming language have different data types. They are classified into the following categories:

1. Basic

These are fundamental data types that contain floating and integer data. Basic data is further divided into four categories:

• Char:- This data type stores a single character in a single byte memory.
• Int: It stores integers, whole numbers with positive or negative values but does not compromise decimals.
• Float:- Float data type in C language represents floating numbers or exponential values.
• Double:- It is similar to float, but the memory size in float data type is 4 bytes, whereas in double, the memory size is 8 bytes.

2. Enumerated

The enumerated data type contains integral constants. The users use the enum keyword to define an integer. Enum acts as a code language that makes it easier for the users to remember the values. The users have the option to change the default settings.

3. Void

As the name suggests, the void is an empty data type with no value. It is used when the programmers do not have to put a return value.

4. Derived

Derived datatypes are those that are not in-built but are derived from in-built data types. Derived data types are divided into the following categories:

• Function:- Function is a specific segment that is used to perform a defined operation or task. It saves time as the user does not have to repeat the same line multiple times while coding.

• Array:- When multiple variables located at different places are collected together, it is termed an array.

• Pointers:- When addresses are represented as symbols, they are called pointers. For example, a pointer saving the address of an integer will be called an integer pointer.

• References:- The alternative name of an existing variable is termed as a reference. It is declared by using &.

Types of Data in Java

A data type in Java suggests how the programmer will use that data. It restricts other functions on that data. Following are the different data types in Java:

1. Primitive:-

The predefined data types in the Java language are called the primitive data types. They are of eight types:

• Char
• Byte
• Short
• Int
• Long
• Float
• Double
• Boolean

2. Non-primitive

Contrary to the primitive data types, the non-primitive ones are not already defined by the Java language but are designed by the programmers.

Career Opportunities in the Data Field

Here are some of the most lucrative career opportunities in the data sector.

1. Data Scientist

If you want to pursue a career in the data sector, a data scientist job might be the best one for you. According to Forbes, one of the most sought-after jobs in America is that of a data scientist. The U.S. News and World Report have also ranked data scientist as the second-best technology job in the US. It is also the sixth-best job in the STEM jobs and ranks 8th amongst all the jobs in the market. The average annual salary of a data scientist in the US is $96,565.

2. Data Architect

The role of a data architect is to build data frameworks and manage databases. According to the Bureau of Labor Statistics, there were 168,000 data architect jobs in the US. By the end of this decade, data architect jobs are expected to grow by 8%. The average annual salary of data architects is $133,840.

3. Data Analyst

Data analysts work closely with data scientists to extract hidden patterns within large datasets and transform them into business-ready insights. According to Indeed, data analyst is one of the highest paying jobs in the data field. The average salary of a data analyst in the US is $75,225 annually.

4. Big Data Engineer

Another promising career path in the data sector is the big data engineer. Their role includes developing, customizing and managing data solutions. The average salary of a big data engineer in the US is $132,571 per year.

How to Learn Data Concepts?

If you want to learn about various data types in detail and other crucial data science concepts, you must pursue a bachelor’s degree or master’s degree in computer science. Another option to learn the theoretical concepts of data programming and acquire practical skills simultaneously is to pursue a certificate course in data science.

You can check out the Advanced Certificate Program in Data Science by upGrad. The course facilities real-time learning through projects. Some of the projects include IMDb movie analysis and analyzing the Uber supply-demand gap. In addition to this, upGrad also provides peer-to-peer learning and mentorship sessions for all its learners.

Conclusion

Data science is undoubtedly one of the most exciting and promising career paths today. It has given birth to some of the most lucrative and exciting career roles, such as data scientist, data engineer, data architect, data analyst, ML engineer, big data developer, IoT engineer, etc. If you wish to bag any of these seven-figure roles, you must start with the basics of data science and scale up to more advanced concepts gradually. This is why a professional data science course is the way to go.

So, check out our top data science courses in the US and enrol today to reap the benefits!

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