Top 20 Data Science Interview Questions and Answers for Beginners


Entering the world of Data Science can feel overwhelming, especially when you're preparing for your first interview. Recruiters often test your understanding of basic concepts, problem-solving skills, and your ability to explain technical ideas clearly. To help you prepare with confidence, here are the top 20 Data Science interview questions and answers for beginners.

1. What is Data Science?

Answer: Data Science is an interdisciplinary field that uses statistics, programming, and domain expertise to extract meaningful insights from data. A good data science course covers data analysis, machine learning, and visualization tools to prepare you for real-world applications.

2. What are the key skills required to become a Data Scientist?

Answer:
  • Python or R
  • Statistics & probability
  • Machine learning basics
  • Data wrangling and cleaning
  • SQL
  • Data visualization (Power BI, Tableau, Matplotlib, Seaborn)

3. What is the difference between structured and unstructured data?

Answer:
  • Structured data: Organized in tables (rows & columns), easy to search.
  • Unstructured data: Includes images, videos, emails, audio, text—harder to process.

4. What is supervised learning?

Answer: A type of machine learning where the model is trained on labeled data. The goal is to predict outcomes based on past examples. Example: predicting house prices.

5. What is unsupervised learning?

Answer: A machine learning method that works on unlabeled data. The model identifies patterns or clusters on its own. Example: customer segmentation.

6. What is overfitting?

Answer: Overfitting happens when a model learns the training data too well—including noise—resulting in poor performance on unseen data.

7. What is underfitting?

Answer: Underfitting occurs when the model is too simple and fails to capture patterns from the training data.

8. What is a confusion matrix?

Answer: A table used to evaluate classification models. It shows True Positives, True Negatives, False Positives, and False Negatives.

9. What is the difference between classification and regression?

Answer:
  • Classification: Predicts categories (spam vs. not spam).
  • Regression: Predicts continuous values (salary prediction).

10. What is feature engineering?

Answer: The process of selecting, transforming, or creating features to improve model performance.

11. What is normalization?

Answer: Normalization scales numerical data into a fixed range, usually 0 to 1, to improve model efficiency.

12. What is a decision tree?

Answer: A machine learning algorithm that splits data into branches based on decision rules, helping make predictions.

13. What is cross-validation?

Answer: A technique to test the performance of a model by splitting data into multiple training and testing sets.

14. What is the difference between variance and bias?

Answer:
  • Bias: Error from incorrect assumptions in the model.
  • Variance: Error due to model sensitivity to small fluctuations in the training data.

15. What is PCA (Principal Component Analysis)?

Answer: PCA is a dimensionality reduction technique that reduces the number of variables while keeping the most important information.

16. What is a hypothesis test?

Answer: A statistical method used to test assumptions using sample data to make decisions about a population.

17. What is correlation?

Answer: A statistical measure showing how two variables move in relation to each other. It ranges from -1 to +1.

18. What is the difference between SQL and NoSQL?

Answer:
  • SQL: Relational databases using structured tables.
  • NoSQL: Non-relational databases handling unstructured or semi-structured data.

19. What does ‘entropy’ mean in machine learning?

Answer: Entropy measures the randomness or impurity in a dataset—used in decision tree algorithms to decide splits.

20. What is a neural network?

Answer: A machine learning model inspired by the human brain. It consists of interconnected nodes (neurons) used for tasks like image recognition and natural language processing.

Conclusion

Preparing for a Data Science interview becomes easier when you know the right concepts. These 20 questions cover the essential fundamentals every beginner must understand. With consistent practice, real-world projects, and hands-on coding, you’ll become more confident and interview-ready in no time.

 

Comments

Post a Comment

Popular posts from this blog

What Is The Difference Between ML Vs DL?

Image
  Introduction If you are exploring a data science course in Noida, you’ve probably come across the terms Machine Learning (ML) and Deep Learning (DL) quite often. Both are hot trends in the tech industry and play a major role in shaping the future of AI. However, many learners are confused about the differences between ML and DL. Let’s break it down in simple words while also connecting it to the latest industry trends. What is Machine Learning (ML)? Machine Learning is a subset of Artificial Intelligence that allows machines to learn from data without being explicitly programmed. In ML, algorithms improve automatically with experience. ML is widely used in industries such as finance, healthcare, marketing, and automation. Many professionals pursuing a data science course in Noida start with ML basics before moving deeper into AI concepts. What is Deep Learning (DL)? Deep Learning is a specialized field within ML that uses artificial neural networks to mimic how the human brai...
Read more
Image
    Introduction In the ever-changing world of technology, a full-stack developer is an invaluable  asset for companies . These experts have both front-end and back-end development skills, which puts them in a strong position for success in software engineering  going forward . Because businesses want someone who can effectively manage the complete development process, full-stack developers have  an abundance of  chances. Nowadays, ambitious people may quickly get the necessary skills by enrolling in a   . full-stack developer course online.  This blog will discuss the importance of full-stack developers, their potential career options, and how easily they can expand their education through online courses . What is a full-stack developer? A proficient web application developer with experience in both front-end and back-end programming is known as a full-stack developer. They manage application logic and databases using server-side technologies lik...
Read more