What are the Top 10 Data Science Interview Questions and Answers?

Students or even graduates greatly misread data science interviews. Know that a data science interview does not ask you complex questions. Instead, they go for the basic ones to see your reactions and delivery process. Just by giving answers, an interviewer can know what your skills are. So instead of practicing tough questions, go for the fundamentals.

For example, I have prepared 10 data science questions for you with answers. G0 through these and tell me what you think.

1. What is data science?
Data science is a field that uses scientific methods and techniques to get insightful information from noisy structured or unstructured data.

2. Difference between data science and data analytics?
Data analytics is a subset of data science. While data analytics revolves around analyzing data, all related tasks and operations fall under data science.

3. What is the difference between data science and data engineering?
While data science uses scientific methods and techniques to read and interpret data, data engineering is concerned with building aspects like databases and storage.

4. What are the best Python frameworks?
Tensorflow and Keras, Numpy, Matplotlib and Pandas

5. What is logistic regression in data science?
It is a statistical analysis method that predicts a binary outcome(yes or no), often based on prior observations of a data set.

6. What is a confusion matrix?
Also called an error matrix, a confusion matrix is a table layout allowing algorithm performance visualization. It is a major part of machine learning and specifically the problem of statistical classification.

7. What is R in Python and used in data science?
It is a language (statistical) used for data analysis and visual representation. While R is good for statistical learning, Python is better for machine learning.

8. Difference between supervised and unsupervised learning?
Supervised and unsupervised learning are used for solving different problems and building models. These are two different types of machine learning techniques.

9. What are the best techniques used for sampling?
Probability sampling & non-probability sampling.

10. What is deep learning?
Deep learning is a kind of machine learning. Deep learning neural networks imitate the structure of the human brain. It is related to artificial intelligence and its functionality.

 

If you want to get some PDFs, there are sources like pdf drive, Academia, Coursera, and DataCamp that have many interview questions and answers PDFs available. However, I suggest looking for shared interview questions by the universities themselves. Yes, universities abroad offer their actual and sample interview questions to help students. If you need any help finding those, let me know.

Data science interviews often revolve around both technical skills and understanding of key concepts. Here’s how you can approach the top ten interview questions in this field: 

1. What is data science? 
It's the science of extracting knowledge and insights from structured and unstructured data using scientific methods, processes, algorithms, and systems. 
2. How do you handle missing data? 
Recommend techniques like deletion, imputation, or using algorithms that support missing data handling directly. 
3. What is overfitting, and how can you avoid it? 
Define overfitting as a common problem where a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. Advise using techniques like simplifying the model, using more data, or applying regularization. 
4. Can you explain what regularization is? 
Regularization helps to solve the overfitting problem by adding a penalty on the different parameters of the model to reduce the freedom of the model thereby simplifying it. 
5. What are the differences between supervised and unsupervised learning? 
Explain that supervised learning algorithms are trained using labeled data, while unsupervised learning algorithms are used when the information used to train is neither classified nor labeled. 
6. What is a confusion matrix? 
Explain that a confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. 
7. How do you ensure your model is generalizable to other datasets? 
Stress the importance of using robust cross-validation techniques and continually testing the model with new data. 
8. What are feature selection methods you use? 
Enumerate several techniques such as filter methods, wrapper methods, and embedded methods, each beneficial depending on the situation. 
9. Explain 'p-value' in layman's terms. 
Describe the p-value as an indicator of how incompatible your data is with a specified statistical model. 
10. What is the difference between clustering and classification? 
Convey that clustering groups a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups, while classification predicts the category to which a new case belongs. 
These questions cover a broad spectrum of fundamental data science knowledge and practices, vital for succeeding in interviews.