Data Science Interview Questions and Answers for Freshers

Data Science Interview Questions and Answers for Freshers in 2026

Key Takeaways

Data Science & Statistics Fundamentals

1. What Is Data Science?

Data science is the process of collecting, cleaning, analysing and modelling data to identify patterns, answer questions and support decision-making. It combines multiple areas, including:

• Statistics
• Programming
• Mathematics
• Data visualisation
• Machine learning
• Business understanding

For example, an e-commerce company may use data science to predict which customers are likely to stop purchasing. The company can then create targeted retention campaigns.

2. What Is the Difference Between Data Science and Data Analytics?

Data analytics mainly focuses on examining historical data to understand what happened and why it happened. Data science can include analytics, but it also uses statistical modelling and machine learning to predict future outcomes or automate decisions.

Example: A data analyst may create a report showing last quarter's customer cancellations. A data scientist may build a machine learning model to predict which customers are likely to cancel next month.

3. What Is the Data Science Life Cycle?

A typical data science life cycle includes:

• Understanding the business problem
• Collecting relevant data
• Cleaning and preparing the data
• Performing exploratory data analysis
• Selecting useful features
• Building a model
• Evaluating the model
• Deploying or presenting the solution
• Monitoring and improving performance

A strong interview answer should begin with the business problem. Building a technically accurate model is not enough if it does not solve the intended problem.

4. What Is Structured and Unstructured Data?

Structured data follows a defined format and can usually be stored in rows and columns. Examples include customer records, sales transactions, employee details and product inventory.

Unstructured data does not follow a fixed tabular structure. Examples include images, videos, emails, social media posts and audio recordings.

Semi-structured formats such as JSON and XML contain organisational markers but may not follow a traditional table format.

5. What Is Data Cleaning?

Data cleaning is the process of identifying and correcting incomplete, inconsistent, duplicated or inaccurate data before analysis. Common data-cleaning activities include:

• Removing duplicate records
• Correcting incorrect data types
• Handling missing values
• Standardising text formats
• Treating outliers
• Fixing inconsistent category names

For example, the values "Chennai," "chennai" and "CHENNAI" may need to be standardised as one category. In practical interviews, candidates may receive a small dataset and be asked how they would inspect its quality before modelling.

6. How Do You Handle Missing Values?

The method depends on the amount of missing data, the type of variable and the reason values are missing. Common approaches include:

• Removing rows with a small number of missing values
• Removing a feature if most of its values are missing
• Filling numerical values using the mean or median
• Filling categorical values using the mode
• Using forward fill or backward fill for suitable ordered data
• Creating a separate "Unknown" category
• Using model-based imputation

Median imputation may be more suitable than mean imputation when numerical data contains extreme values. Candidates should not say that missing values must always be deleted — the decision must be based on the dataset and business context.

7. What Is an Outlier?

An outlier is an observation that differs significantly from most other observations. Outliers may be caused by data-entry errors, measurement errors, rare but valid events, natural variation or fraudulent activity.

Common methods used to detect outliers include box plots, interquartile range, Z-scores, scatter plots and domain-specific rules. An outlier should not be removed automatically — for example, an unusually large bank transaction may be important when developing a fraud-detection system.

8. What Is Exploratory Data Analysis?

Exploratory data analysis, or EDA, is the process of examining a dataset before building a model. EDA helps identify missing values, data distributions, relationships between variables, outliers, duplicate records, class imbalance and possible data-quality issues.

Common EDA methods include summary statistics, histograms, bar charts, box plots, scatter plots and correlation analysis. The objective is to understand the data and form useful questions, not simply generate multiple charts.

9. What Is the Difference Between Mean, Median and Mode?

Mean is the sum of all values divided by the number of values. Median is the middle value after arranging values in order. Mode is the most frequently occurring value.

Example: Consider the salary values ₹20,000, ₹22,000, ₹25,000, ₹27,000 and ₹2,00,000. The unusually high salary significantly increases the mean. The median may therefore provide a more representative measure of the typical salary.

10. What Is Standard Deviation?

Standard deviation measures how widely values are spread around the mean. A small standard deviation indicates that values are relatively close to the mean. A large standard deviation indicates greater variation.

For example, two teams may have the same average performance score, but the team with the larger standard deviation has less consistent performance.

11. What Is Correlation?

Correlation measures the strength and direction of the relationship between two variables. A positive correlation means the variables tend to increase together. A negative correlation means one tends to decrease when the other increases.

Python Interview Questions

12. What Is the Difference Between a List and a Tuple in Python?

A Python list is mutable, meaning its elements can be changed after creation. A tuple is immutable, meaning its elements cannot be changed after creation.

skills = ["Python", "SQL", "Statistics"]
skills.append("Machine Learning")

tools = ("Pandas", "NumPy", "Scikit-learn")

Lists are useful for collections that may change. Tuples are useful for fixed collections. Python's official tutorial identifies lists, tuples, sets and dictionaries as core data structures that beginners should understand.

13. What Is a Python Dictionary?

A dictionary stores information as key-value pairs.

candidate = {
    "name": "Arun",
    "skill": "Python",
    "experience": 0
}

Here, name, skill and experience are keys. Dictionaries are useful when values must be retrieved using meaningful labels rather than numerical positions.

14. What Are Pandas Used For?

Pandas is a Python library used for loading, cleaning, transforming and analysing structured data. Important Pandas operations include:

• Reading CSV and Excel files
• Selecting rows and columns
• Filtering records
• Handling missing values
• Combining datasets
• Grouping and aggregating data
• Creating summary statistics

The Pandas groupby() operation follows the split-apply-combine approach: data is divided into groups, a calculation is applied and the results are combined.

15. What Is the Difference Between loc and iloc?

loc selects data using row or column labels. iloc selects data using integer positions.

df.loc[0, "salary"]
df.iloc[0, 2]

The first expression uses a row label and column name. The second uses positional indexes. Freshers should be able to explain this difference and demonstrate basic filtering with Pandas.

SQL Interview Questions

16. What Is SQL and Why Is It Important for Data Science?

SQL is used to retrieve, filter, combine and aggregate data stored in relational databases. Data scientists use SQL to extract required records, join multiple tables, calculate business metrics, group results, identify trends and prepare data for analysis.

A fresher should understand SELECT, WHERE, GROUP BY, HAVING, ORDER BY, subqueries, aggregate functions and joins. PostgreSQL documentation explains that GROUP BY combines rows sharing common values, while HAVING filters groups after aggregation.

17. What Is the Difference Between WHERE and HAVING?

WHERE filters individual rows before grouping. HAVING filters grouped results after GROUP BY.

SELECT department, AVG(salary)
FROM employees
WHERE status = 'Active'
GROUP BY department
HAVING AVG(salary) > 50000;

In this query, WHERE keeps only active employees, GROUP BY organises them by department, and HAVING keeps departments whose average salary exceeds ₹50,000.

18. What Is a SQL JOIN?

A JOIN combines rows from two or more tables using a related column. Common joins include:

• INNER JOIN: Returns matching records from both tables
• LEFT JOIN: Returns all records from the left table and matching records from the right
• RIGHT JOIN: Returns all records from the right table and matching records from the left
• FULL OUTER JOIN: Returns matched and unmatched records from both tables

During an interview, explain joins using a simple example such as combining a customer table with an orders table.

Machine Learning Interview Questions

19. What Is Machine Learning?

Machine learning is a method through which systems learn patterns from data and use those patterns to make predictions or decisions. Examples include predicting house prices, detecting fraudulent transactions, classifying emails as spam, recommending products and forecasting customer demand.

Machine learning is one part of data science. A complete data science project also requires problem understanding, data preparation, evaluation and communication.

20. What Is the Difference Between Supervised and Unsupervised Learning?

Supervised learning uses labelled data. The model learns from examples that include both input features and known outputs — for example, price prediction, churn prediction and spam classification.

Unsupervised learning uses data without known output labels — for example, customer segmentation, pattern discovery, grouping similar products and dimensionality reduction.

Regression and classification are common supervised-learning tasks, while clustering is a common unsupervised-learning task.

21. What Is the Difference Between Classification and Regression?

The target variable determines whether a problem is mainly classification or regression.

22. What Is Overfitting?

Overfitting occurs when a model learns the training data too closely, including noise and random patterns, but performs poorly on unseen data. Possible signs include very high training accuracy, much lower testing accuracy and an unnecessarily complex model.

Ways to reduce overfitting include collecting more representative data, reducing unnecessary features, using regularisation, applying cross-validation, pruning decision trees and using early stopping.

Scikit-learn warns that evaluating a model on the same data used for training can create misleadingly strong results. It recommends holding out test data and using suitable cross-validation methods during model development.

23. What Is Underfitting?

Underfitting occurs when a model is too simple to learn important patterns in the data. An underfit model performs poorly on both training and testing data.

Possible solutions include adding meaningful features, selecting a more appropriate algorithm, reducing excessive regularisation, increasing model complexity and improving data quality.

24. What Is a Train-Test Split?

A train-test split divides available data into separate portions. The training set is used to teach the model. The test set is used to evaluate how well the trained model performs on unseen data.

A common mistake is making preprocessing decisions using the complete dataset before splitting it. This can allow information from the test data to influence training and create data leakage. The final test set should remain separate until the model and major decisions are finalised.

25. What Is Cross-Validation?

Cross-validation evaluates a model using multiple training and validation combinations. In k-fold cross-validation:

• The training data is divided into k sections
• The model is trained on k−1 sections
• The remaining section is used for validation
• The process repeats until every section has been used for validation
• The evaluation scores are averaged

Cross-validation provides a more reliable estimate than relying on only one random validation split. However, time-series and grouped datasets require suitable splitting methods rather than ordinary random folds.

26. What Is Accuracy?

Accuracy is the proportion of total predictions that are correct. Accuracy can be misleading when classes are imbalanced.

Suppose only 1% of transactions are fraudulent. A model that predicts every transaction as legitimate would achieve 99% accuracy but fail to detect any fraud. In such cases, precision, recall, F1-score and the confusion matrix may be more informative.

27. What Are Precision and Recall?

Precision measures how many predicted positive cases were actually positive. Recall measures how many actual positive cases the model successfully identified.

In fraud detection, high precision means most flagged transactions are genuinely suspicious, while high recall means the model identifies most fraudulent transactions. The correct metric depends on the cost of different errors.

28. What Is a Confusion Matrix?

A confusion matrix summarises classification results using true positives, true negatives, false positives and false negatives.

It helps interviewers understand whether a candidate can look beyond a single accuracy score. For example, in disease screening, a false negative may be more serious than a false positive — the evaluation strategy should reflect that risk.

Project & Career Questions

29. How Should You Explain a Data Science Project?

Use the following structure:

• Problem: What business or user problem did you address?
• Data: Where did the dataset come from?
• Preparation: How did you clean and transform it?
• Analysis: What patterns did you discover?
• Model: Which algorithms did you compare?
• Evaluation: Which metrics did you use and why?
• Result: What did the model or analysis reveal?
• Limitations: What could reduce reliability?
• Next step: How would you improve or deploy it?

Avoid spending the entire answer describing algorithms. Interviewers want to understand your reasoning and contribution.

30. Why Do You Want to Become a Data Scientist?

A suitable fresher answer could be: "I am interested in data science because it combines problem-solving, programming and analytical thinking. While completing my projects, I enjoyed cleaning data, identifying patterns and presenting conclusions that could support a business decision. I now want to build these skills in a professional environment and learn from real datasets and experienced teams."

Personalise the answer using your actual experience. Avoid memorised claims that cannot be supported with examples.

How to Prepare for a Data Science Interview in 2026

Follow this seven-step plan:

• Revise Python fundamentals and practise short coding problems
• Write SQL queries involving joins, grouping and subqueries
• Review descriptive statistics and probability basics
• Practise data cleaning and EDA using real datasets
• Understand common machine learning algorithms conceptually
• Prepare two projects using the problem-to-result structure
• Practise explaining answers aloud in simple language

Common Interview Mistakes Freshers Should Avoid

Final Thoughts

Preparing Data Science Interview Questions and Answers for Freshers is not only about memorising technical definitions. Recruiters want to know whether you can understand data, choose a logical approach, evaluate results and communicate your findings clearly. Focus on Python, SQL, statistics, data cleaning, machine learning fundamentals and project explanation. Practise with realistic datasets, review your mistakes and prepare concise examples from your own work.

To strengthen your practical skills, explore TechPanda's Data Science Course in Chennai, where learners can work on guided projects, practise interview questions and receive career preparation and placement assistance.

Frequently Asked Questions