Interview Questions on Data Types & Measurements

Introduction to Data Types and Measurements in Data Science

In today’s ever-evolving world of data science, understanding the types of data is crucial for developing reliable models and making informed decisions. From simple datasets to complex, high-volume information sets, the ability to classify and analyze data is an essential skill. The structural differences in data define its primary use: structured data is organized in tables with specific types of information per column, while unstructured data consists of raw information that often requires more processing but can lead to quicker insights in the analytics process.

This blog explores some of the foundational concepts related to types and scales of data, including the differences between nominal, ordinal, continuous, and discrete data. It also highlights the significance of dummy variables and provides an overview of how these concepts play a critical role in data science. Once you grasp these building blocks, you'll be better equipped to analyze data and make confident, data-driven decisions.

1. What is Nominal Data?

Name of categories (there is no natural order among categories).

There is no inherent order and has a limited set of entries.

Usually, nominal data is either alphabetical (string) or in text format.

Nominal data has to be converted into a dummy variable encoding format for ML algorithms to understand the same.

2. What is Ordinal Data?

Categories that have particular order (Inherent order).

Ordinal data has to be converted to its numeric equivalent using encoding techniques.

Eg: Shirt size: S, M, L, XL, XXL; Gate numbers in the airport: 1, 2, 3, 4.....

The difference in the different levels or values of the ordinal data is consistent in direction and consistency need not be in magnitude.

The difference in the different levels has no meaning.

We can perform 'Count' as well as 'Rank' the items in an order.

Start learning the fundamentals of data science with a Data Science Course in Chennai to dive deeper into such concepts.

3. What is Interval Data?

Interval scales are the numeric scales where we know both the order of the values along with the exact differences between the values.

The difference between the levels has a meaningful rationale.

No natural zero (Absence of absolute zero). This means, if the temperature is zero, it does not mean there is no temperature.

Eg: Time, Temperature, Date, and IQ level.

We can perform mathematical operations - Addition & Subtraction

4. What is the Ratio?

Ratio data is very much like the interval data – the values must be numerical where the difference between points is standardized and quite meaningful.

Whereas, in order for data to be considered as the ratio data, it must have a true zero value, which means ratio data cannot have negative values.

Eg: Height, Weight, etc. If we have zero money then it means there is no money.

We can perform mathematical operations such as Addition, Subtraction, Multiplication, and Division.

5. What is a Factor?

The factor is a variable, which can take a limited set of values.

For example: 'Gender' is a variable that can take two levels - 'Male' & 'Female'.

Another example is 'Month', which can take '12' levels - Jan, Feb, Mar,....., Dec.

6. What are the broad classifications of data types?

Broadly speaking data can be classified as Continuous data and Discrete data.

Discrete data can be further classified as Categorical data and Count data.

Categorical data is further classified as Binary categorical data and Multiple categorical data.

Multiple categorical data is further classified as Nominal data and Ordinal data.

Continuous data & Count data are considered Quantitative data, whereas Categorical data is considered as Qualitative data.

7.Differences Between Structured, Semi-Structured, and Unstructured Data

8. Difference Between Big Data and Non-Big Data

9. Differences Between Cross-Sectional, Time Series, and Longitudinal Data

10. Differences Between Balanced and Imbalanced/Rare Datasets

11. Differences Between Offline and Online Processing

12. What is a Random Variable?

Any variable whose output varies and has a chance associated with the output values is called as Random variable.

Eg: Flipping a coin has Head or Tail as output and Flipping a coin is a random variable. Note: Random Variables are always represented using capital letter and values, which are not random variables are represented using small letter.

13. What are Measurement levels?

Measurement levels are a way to interpret the calculations that can be applied to the data for extracting the information. There are 4 levels of measurements that we can learn: Nominal, Ordinal, Interval, and Ratio.

14. What does Nominal type in measurement levels mean?

Name of Categories (There is no natural order among categories) There is no inherent order.

Eg: Color names, Gender

15.What is the ordinal measurement level?

Categories that have Particular order (Inherent order).

Eg: Shirt size : S, M, L, XL, XXL.

16. What does Interval measurement level represent?

The Interval level is a numeric measure of the data. This numeric measure will explain the relative value of a data point in the data set. The values will always lie in a defined boundary. Hence these values are said to be a measure of local scale.

Eg: Temperature, and Date.

17. What is a Ratio measure?

Ratio data is very much like the interval data – the values must be numerical where the difference between points is standardized and quite meaningful. Whereas, for data to be considered as the ratio data, it must have a true zero value, which means ratio data cannot have negative values.

Eg: Height, Weight.

18. What is the Factor variable?

The Factor variable is nothing but it has limited values (or) labels.

Eg: Month (Jan, Feb, …., Dec) ---- Only 12 values for Month variable.

19. What are Continuous and Discrete Data?

Advance your understanding of these data types with a Data Science Course in Bangalore tailored to industry demands.

20. Difference Between Qualitative and Quantitative Data

21. What is Binary Data?

Binary data is a type of categorical data with only two possible outcomes. These outcomes are often represented as 0 and 1, true and false, or yes and no. For instance, in a dataset tracking customer purchases, the binary variable "Purchased" could have values such as 1 (if the customer made a purchase) and 0 (if not).

22. What are Dummy Variables?

Dummy variables, also called indicator variables, are numerical representations of categorical data. They are commonly used in statistical modeling to encode categories as binary values. For example, if a variable "Gender" has categories "Male" and "Female," dummy variables might represent Male as 1 and Female as 0, making it easier to analyze using mathematical models.

23. Difference Between Nominal and Dichotomous Data

Sharpen your skills on data handling and measurements with a comprehensive Data Science Course in Hyderabad designed for aspiring professionals.

24. What are Interval-Censored and Right-Censored Data?

Interval-Censored Data: This refers to data where the exact value is unknown, but it lies within a specific range. For example, if a medical test shows a patient’s blood sugar level is between 90 and 120 mg/dL, the data is interval-censored.

Right-Censored Data: This refers to cases where the value is not observed above a certain threshold. For instance, in survival analysis, if a study ends before some participants experience the event of interest (e.g., death), their data is right-censored.

25. What is the difference between Qualitative Variables and Quantitative Variables?

Qualitative Variables: Variables that categorize or label data, such as hair color, nationality, or types of music genres. These are descriptive and do not have numerical values.

Quantitative Variables: Variables that represent measurable quantities, such as income, weight, or distance traveled. These are numeric and allow for mathematical operations.

26. What is a Derived Variable?

A derived variable is a new variable created using existing data. It is often calculated or transformed based on other variables in a dataset. For example, if a dataset includes "Date of Birth" and "Current Date," a derived variable "Age" can be computed.

27. What are Polytomous Variables?

Polytomous variables are categorical variables that have more than two categories. For instance, "Education Level" could have categories like "High School," "Bachelor's," "Master's," and "Doctorate." These variables can be ordinal if the categories have a logical order or nominal if they do not.

28. What is the difference between Data Levels and Data Scales?

Data Levels: Refer to the way data is categorized, ordered, or measured, such as nominal, ordinal, interval, and ratio levels.

Data Scales: Represent the tools or methods used to measure the data, such as a thermometer for temperature or a weighing scale for weight. Scales are typically associated with interval and ratio data.

29. What is a Latent Variable?

A latent variable is a variable that is not directly observed but is inferred from other observable variables. For instance, "intelligence" is a latent variable often inferred through test scores, academic performance, and problem-solving abilities.

30. What is a Continuous Scale?

A continuous scale represents data that can take any value within a range. Examples include temperature measured in Celsius or Fahrenheit, or distance measured in kilometers or miles. Continuous scales are associated with interval and ratio data.

31. Difference Between Aggregated and Disaggregated Data

32. What are Dummy Variables in Regression Analysis?

In regression analysis, dummy variables are used to include categorical data in a numerical model. For example, to include "Marital Status" with categories "Single," "Married," and "Divorced" in a regression model, two dummy variables can be created to represent these categories numerically.

Conclusion

Understanding data types and measurement levels is fundamental for anyone delving into data-driven fields such as data science, analytics, and research. These concepts form the backbone of data collection, organization, and analysis. From distinguishing between nominal and ordinal data to comprehending the nuances of continuous and discrete variables, each type of data plays a crucial role in deriving actionable insights.

At 360DigiTMG, we help learners develop a deep understanding of these foundational concepts through our comprehensive courses and hands-on training. Our expert-led programs are designed to simplify complex topics, such as nominal and ordinal data, dummy variables, and advanced measurement techniques, ensuring learners are well-equipped to handle real-world datasets. By offering practical exposure, interactive sessions, and industry-relevant projects, 360DigiTMG ensures that students not only grasp theoretical knowledge but also build the confidence to excel in interviews and professional scenarios.

Whether you're starting your journey in data science or looking to refine your skills, 360DigiTMG provides the guidance and resources necessary to master these essential topics, setting you up for success in the competitive data science landscape.

FAQs

1. What is the Importance of Understanding Data Types in Data Science?

Recognizing the different types of data is crucial because it helps you choose the right analysis methods and models. Knowing whether your data is nominal, ordinal, or continuous can influence the way you interpret patterns and relationships.

2. How Do Data Types Affect Data Processing?

Different data types require distinct processing techniques. For instance, numerical data can be analyzed using statistical methods, while categorical data may need to be encoded into a numerical format before analysis. Understanding these differences is key to effective data processing.

For example, numerical data can be analyzed with statistical tools, whereas categorical data may need encoding techniques to be included in machine learning models.

3. Why is it Necessary to Convert Data Types for Machine Learning?

Machine learning models work best with numeric data. Converting categorical data into numerical formats, such as using dummy variables, enables models to process and analyze the information effectively.

4. Can Unstructured Data Be Analyzed?

Yes, while unstructured data like images, text, or videos can be difficult to analyze, advancements in machine learning techniques such as NLP (Natural Language Processing) and computer vision have made it possible to extract insights from such data.

5. How Do Data Types Influence the Quality of Insights?

The data type determines how you can interpret the data and draw conclusions. For instance, while ordinal data shows ranking or order, continuous data provides precise values that enable more detailed analysis, which can impact the decision-making process.