What to Expect in a Data Science Interview: Common Questions and Answers

Summary

(Preparing for a data science interview? This blog lists the most commonly asked questions with expert-crafted answers to help freshers and professionals succeed. Covering topics like Python, statistics, machine learning, and real-world scenarios, it’s a must-read guide to boost your confidence and crack your next data science interview.)

The data science industry is growing rapidly after the introduction of artificial intelligence. As a result, technology and software are developing every day, which increases the demand for this job. Data scientists collect raw data and transform it into actionable strategies.

 

Want to build a dream career in the data science domain? Make yourself prepared to crack any job interview with these common data science interview questions and answers.

Basic Data Scientist Interview Questions

These are some basic-level data science interview questions that test your skills and qualifications:

1. What do you mean by data science?

Importance: The interviewer asks this question to test your basic understanding of data science.

Answer: Data science is the procedure of utilising various computational and mathematical techniques to figure out meaningful insights from large datasets.

2. What are the differences between supervised and unsupervised learning?

Importance: The interviewer asked this question to test your knowledge of foundational concepts of data science.

Answer: Supervised learning utilises labelled data as input and prioritises a feedback mechanism. On the other hand, unsupervised learning uses unlabelled data as input and does not have a feedback mechanism.

3. What do you mean by a decision tree?

Importance: The interview asks this question to analyse your understanding of different tools in data science.

Answer: A decision tree is a tool used to categorise data and analyse the possibility of outcomes in a system. The base of the tree is considered the root node, which branches out into decision nodes based on the various decisions made at each stage.

4. What is a Confusion Matrix?

Importance: The interviewer asks this question to evaluate your problem-solving skills in this field.

Answer: A Confusion Matrix is the prediction results of a particular problem in data analysis and describes the model's overall performance in a n*n matrix.

5. Why is a p-value significant?

Importance: The importance of asking this question is to analyse your skill of finding results.

Answer: The p-value represents the probability of an observation made about a dataset as a random chance. A p-value of less than 5% refutes the null hypothesis and decreases the validity of a result.

Also Read:- How to Answer Digital Marketing Interview Questions Like a Pro?

Intermediate Interview Questions for a Data Scientist

These are intermediate-level data science interview questions that test your ability to apply your knowledge of data science to live projects.

1. How is data analytics different from data science?

Importance: Answering this question will help showcase your understanding of basic concepts in data science.

Answer: The primary difference between data science and data analytics is that data science considers extracting data to use insights and address business problems. On the other hand, data analytics is a broad practice of finding the correlations and patterns of a dataset.

2. Differentiate between univariate, bivariate, and multivariate analysis.

Importance: This question is essential for gauging your understanding of variable comparisons.

Answer: An univariate analysis includes analysing a single variable, while a bivariate analysis means comparing two. However, a multivariate analysis involves comparing two or more variables.

3. What is the process of logistic regression done?

Importance: The interviewer asks this question to examine your knowledge of different data analysis tools.

Answer: A logistic regression or the logit model is a procedure used to predict a binary outcome using a linear array of predictor variables.

4. Explain Naive Bayes.

Importance: The interviewer asks this question to you to test your data analysis skills.

Answer: Naive Bayes is a classification procedure that assumes that all features under evaluation are independent. It is known as naive because it makes the same assumption, which is frequently unrealistic for real-world data.

5. What is overfitting and how can you avoid it?

Importance: Answering this question is important to analyse your knowledge of the foundational concepts of data science.

Answer: Overfitting happens if a model performs well on training data and is poor with new data. You can avoid overfitting using methods like pruning, regularisation, and cross-validation.

Advanced Interview Questions in Data Science

Here are some advanced-level data science interview questions which analyse your ability to think critically in data science projects:

1. How should you maintain a deployed model?

Importance: The interviewer asks this question to analyse your ability to maintain a deployed model in data science.

Answer: To maintain a deployed model, you can train the data with new values or create a new model if an existing model starts producing inaccurate results.

2. Mention some common sampling techniques.

Importance: This question is important to evaluate your skills for collecting data.

Answer: Some common sampling techniques are:

• Systematic Sampling
• Simple Random Sampling
• Purposive Sampling
• Convenience Sampling

3. How to compare an error and a residual Error?

Importance: Answering this question is crucial to detect any errors in the model performance.

Answer: Error calculates the limitation to which an observed value results from an actual value. On the other hand, a residual error describes the difference between an observed value and the estimated value of specific data points.

4. What is A/B testing?

Importance: This question is important to test your data analytics skills for attracting customers. 

Answer: A/B testing is the procedure that businesses use to predict the needs and preferences of customers.

5. What is the importance of feature scaling?

Importance: The interviewer asks this question to analyse your understanding of the usage of machine learning in data science.

Answer: Feature scaling keeps the independent variables normal to make sure that no single variable dominates the model, mostly in algorithms that calculate the distance.

Final Words

Preparing these above data science interview questions and answers is useful to crack data science job interviews. You may also choose Amity Online and enroll with its Master's of Business, which provides you with the knowledge of data-driven technologies and tools. You may also get expertise in using software like Python, Spark, MySQL, and Hadoop. Hurry up and contact us today to get prepared for a data science interview!