Data Science Interview Questions For Freshers

Data Science Interview Questions for Freshers: The 2026 Guide

Hello freshers of the IT field, if you are someone who is looking to land a data science job in 2026 you’ve probably noticed that companies are over the "certificate" phase. They are not just asking you to know if you can import a library in Python but what they want you to know is whether you can actually think like a data scientist first. Most freshers walk into interviews with their ready to recite definitions but some moment comes when an interviewer asks "Why did you pick that model for the analysis that you performed?" and they freeze over it.

This guide for Data Science Interview Questions is not about memorizing the answers but understanding the question. It’s mostly about understanding how data actually moves from a messy CSV file to a board-room insight within a few steps. We here have broken down the most common questions that are asked for fresher data science interviews into sections that cover the math beyond the tools as well as the logic for them which you’ll need to survive the technical rounds of first time interview.

Section 1 The Foundation: Statistics and Probability

1. Tell me what is the difference between population and sample, and how they are related?

So to explain the population it can be considered as the entire group you want to study. For example, like every single smartphone user in India, every household in Pune etc. But because you can't talk to everyone in the entire population you take a Sample out of it. Which means it is just a smaller but also a manageable subset of that group. The goal here for us is to make sure that the sample is diverse enough and must have representation of the whole population without any specific bias or over representation of one group only.

2. Can you explain what is the Normal Distribution and why it matters in any kind of statistical analysis?

The Normal Distribution is that classic "Bell Curve" shaped distribution visualized on a graph where most of the data points sit in the middle. While the extreme ends of this graph taper off. It’s a big deal because many natural phenomena like heights or test scores follow this bell shape of distribution pattern. In Data Science many algorithms that are used for statistical analysis assume your data is normally distributed, and you need to feed them data that is normally distributed to work accurately.

3. What is the difference between Type I and Type II errors?

These types of errors are what may occur if your assumptions about the data are wrong or if the data is mislabelled. Mostly, even misrepresentation of the data happens by you, and may cause these kinds of errors. Below is the classification-

Type I, also called False Positive: You claim something is happening when it’s actually not, such as this example, where the alarm goes off but there’s no fire. Or data says a crash is coming, but it doesn't happen at all.
Type II, also called False Negative: You fail to catch something that is actually happening. Say, for example, there is a big fire, but your alarm stays silent. Or say a market crash occurred, but your model says it is getting higher.

4. How do you handle missing or corrupted data in a dataset?

You cannot change the corrupted data to better, as it may be based on assumptions, so just ignore it. Usually, in your analysis, you can either delete the rows if the missing data is small or fill them in with the mean or median, called Imputation. Nowadays, you can also use an algorithm that can handle NaN values instead of any filling. The choices of what to do must depend on how much data you’re losing and how much is needed for proper confidence in your analysis.

5. What is the meaning of A/B Testing, and how does a Data Scientist use it?

A/B testing is basically a controlled experiment in your dataset. You first show version A of the product to one group and version B to another. You then use statistics to see which one performed better based on a specific metric, such as the click-through rates or even actual sign-ups.

Section 2 Python and Data Manipulation

6. Why is Python preferred over other languages for Data Science?

It mostly comes down to the ecosystem around it. Python already has libraries like Pandas, NumPy, and Scikit-learn, which handle most of the heavy work for you, so you are not building everything from scratch. Also, the syntax is simple to read. Because of this, you don’t spend time figuring out what the code is doing. And now, instead, you can even focus more on solving the actual problem.

7. What is the difference between a List and a Tuple in Python?

Lists are something you can change anytime. You can add values as well as remove them or even modify existing ones as your program runs. Tuples, on the other hand, are fixed once you create them. You cannot change them later, so they are mostly used where the data should stay constant and not be touched.

8. Explain the difference between 'loc' and 'iloc' in Pandas.

loc is used when you are working with labels. So if your dataset has column names or row names, you directly use those to pull the data.

iloc is more about position. It does not care about names, and it just looks at index numbers like the first row as well as the second column, and so on.

9. What is a Lambda function?

A lambda function is like a quick shortcut function that you write in one line. You don’t formally define it using def and all that. Mostly used when you just need a small piece of logic for a short time, like applying some simple operation on a list or a column.

10. How does NumPy handle arrays differently from standard Python lists?

NumPy arrays are built for performance. They are faster as well as more memory efficient compared to normal Python lists. Also, one big difference is that you can apply operations on the whole array at once instead of looping through each value one by one, which saves a lot of time when your data gets large

Section 3 Machine Learning (The Logic)

11. What is the difference between Supervised and Unsupervised Learning?

This mostly depends on whether your model is being guided or not. In supervised learning, you already have labeled data, so you are telling the model what is what, like this is a cat and this is a dog, and it learns from that. In unsupervised learning, you don’t give any labels; you just pass the raw data, and the model tries to figure out patterns on its own, like grouping similar customers together based on how they shop.

12. Can you explain the meaning of the Bias-Variance tradeoff?

Bias is when your model is too basic, and it misses the actual pattern in the data, so it underfits. Variance is the opposite, where the model becomes too sensitive and starts picking up random noise, so it overfits. What you are trying to do is find a middle point where the model learns properly but also works well on new data.

13. How does a Decision Tree decide where to "split"?

A decision tree keeps checking which feature is giving the cleanest separation in the data. It uses things like information gain or gini index for that but in simple terms it just tries to split the data in a way where groups become more clear. Thus the algorithm will keep doing this step by step at least not until it reaches a point where it resolves the data and finds a clear cluster.

14. What is a Random Forest algorithm which is widely used in ML applications and you must also explain why it is better than a single tree method?

So one might imagine the random forest algorithm like multiple decision trees working together and not just a single decision tree. One tree can easily make a wrong decision or get biased but when you have many trees and you take their combined output the overall result becomes more stable. That is why it usually performs better and does not overfit as easily as a single tree.

15. What are the common evaluation metrics for classification?

A lot of freshers just say accuracy and stop there but that is not always enough. If your data is unbalanced as well as accuracy can give a false idea. So you look at other things also.

Precision is when you check out of all the positive predictions how many were actually correct.

Recall is when you check out of all the actual positive cases how many your model was able to catch.

F1 score is basically a balance between precision and recall so you get a more practical idea of performance.

Section 4 SQL and Database Queries

16. Why is SQL-based learning still mandatory for Data Scientists in 2026?

In real jobs your data is not sitting nicely in Excel sheets waiting for you. Most of it is stored inside databases and if you don’t know how to pull it out properly you end up depending on someone else every time. SQL is still one of the most important strategies for data and wrangling which is able to bring exactly what you need for your analysis together. So instead of guessing or working with incomplete data you can make the proper final set before analysis starts.

17. What is the difference between a LEFT JOIN and an INNER JOIN?

The Inner join is what only gives you the rows that are matched to tables that have matching reference column data. So if something does not match it simply won’t show up in the result.

Whereas a Left join keeps everything from the left table no matter what and then tries to match the right column or reference side with the data inside the rows. If nothing matches there you will still see the row but with null values for the missing part in left.

18. What is the meaning of a Subquery and when would you use one instead?

A subquery is just a way of having to write one query inside another. This is done when a single step is not enough for querying all data. For example first you calculate something like an average, and then you use that result to filter your main data. This strategy will help you when the logic needs to be broken into several parts, and not do everything in one go.

19. What are the Window Functions such as the RANK() or ROW_NUMBER() and what are they used for?

The kind of window functions are used when you want to do calculations across related rows, but then you need to keep every row visible in the output, irrespective of the results. Say you want to rank people city-wise, you don’t want to merge everything like group by does. Window functions let you do that ranking while still seeing all the individual rows.

20. How can you handle duplicate records within your datasets using a SQL table?

If you just want a clean output of the dataset without any duplicate values, you can use the tools for distinct and get unique values tab in SQL. But if duplicates actually need to be removed from the table then you first identify them using something like row number and then delete the extra ones while keeping a single correct record.

Conclusion Beyond the Technicals

Getting into data science as a fresher is not only about giving correct answers in an interview. What people really notice is how you think and how you approach a problem when you don’t immediately know the answer. Tools and technologies for data science and overall data analysis will keep changing, but the way you handle data and logic is what stays with you forever.

If all of this feels data science tools and logic feel a bit heavy in the beginning, it is fine. The best way forward is to work on things that feel close to real job scenarios instead of only reading concepts. At Sevenmentor Institute, the Data Science Course is designed in a way that you actually practice these situations. It is not only about learning code but also about facing interview-type questions and working on projects that feel practical. That kind of exposure usually makes a big difference when you are trying to move from the learning phase to actually getting hired.

Frequently Asked Questions: What the 2026 Market Actually Expects

1. Is it actually possible to land a high-paying Data Science job in 2026 without having a Master’s degree?

The honest truth is that it’s absolutely possible. But you have to be ready to work twice as hard on your actual portfolio with proper projects to show that you have the complete knowledge and skills. Also, you will need to prove your mettle in interviews with clear and logical explanations for each question.

2. How much coding skills do I really need to have under my belt for a Fresher role in Data Science?

You do not exactly need to be a full-blown software engineer or perfect coder to be in data science. It is completely normal if you have only known the basics of the terminal or the command line. Over time, however, you should be comfortable enough with SQL to pull your own data as well as have enough skills in Python to use libraries like Pandas and Scikit-learn. Without these skills, you may be constantly checking the manual or going through a basic tutorial again and again.

3. What is the one major thing that freshers usually mess up during their Data Science interviews?

In our assessment, most people who are going for fresh interviews are focused way too much on complex math and completely forget about the logical reason for what they are doing. Say if you build a model with 99% accuracy, but you can't actually explain how that model helps a company save money or find new customers, the interviewer isn't going to be impressed at all. So be prepared for such logical explanations instead of rote knowledge or even just building the model.

4. Should I be focusing more on Deep Learning like things in my portfolio or just stick to the Basic Algorithms first?

You should definitely stick to the basics first, as you are considered fresher and not expected to have knowledge of deep learning or even machine learning for that matter. Most companies in 2026 aren't actually running complex neural networks every single day, but rather running Regressions or even Random Forests as well as XGBoost to solve their daily problems. You need to master the background of these algorithms before jumping to anything else, okay.

5. Where can I actually get some help regarding the data science interview preparation, because I’m struggling?

Don't worry, students, it is easy to get lost in the complexity of data science and its associated ML sectors. We at SevenMentor provide a detailed curriculum as well as proper interview and resume preparation in our data science course across India. We have successfully created students throughout India who have cracked the interviews and made high-paying salaries in MNCS now.