## Introduction

With the rise of the digital world over the past few decades, immense data is being generated every day. As companies like Google, Facebook, etc. recognized the potential of the piles of data, other non-digital industries soon followed the trend and started utilizing the power of paying with their data. This resulted in a drastic increase in the number of job opportunities associated with Data Science and Analytics. As per Dataflair, a Data Scientist’s average salary is INR 8,18,099, and there are over 1,000 Data Scientist job postings for every million postings each month.

Data Science interviews are very different from any other technical interview, and that is mostly because of the nature of the work that a Data Scientist performs. You may be asked questions about Statistics, Probability, Machine Learning, Data Visualization, and some behavioral questions. Let’s list out the important Data Science interview questions and answers.

**What are your assumptions for linear regression?****Why do we need deep learning over machine learning?****What is overfitting?****How to prevent overfitting?****Why do we need activation functions?****What are the differences between supervised and unsupervised learning?****What is the ‘power’ of a hypothesis test?****Explain how you will handle missing values in our data?****Lay out the differences between a histogram and a bar graph?****How do you decide the value of K for K-Means clustering?****When do we confidently declare that a time series has become stationary?****What are the different types of joins in data queries?****Elaborate the steps in the making of a decision tree?****When do we opt for resampling?**

Now let’s, deep dive!

**1. What are your assumptions for linear regression?**

This is one of the fundamental Data Science interview questions.

I have three major assumptions.

1. The data should have a linear relationship, which is pretty obvious because it’s linear regression and you cannot apply it on non-linear data just as is.

2. There should be multivariate normality, and the residuals should be normally distributed.

3. There should not be an unequal variance in the data.

**2. Why do we need Deep Learning over Machine Learning?**

**Tip:** This is a bit of an advanced Data Science interview question.

Machine Learning fails to improve accuracy when data grows larger. The algorithms get saturated with more data, and weights do not change as expected. Hence, Deep Learning is preferred for bigger datasets that make use of the Neural Network to provide a better accuracy slope. It is also beneficial in non-structured data like images, texts, etc.

**3. What is overfitting?**

**Tip:** This Data Science interview question is asked very frequently.

When a model is trained intensively over a similar type of dataset, it tends to learn only that type of behavior. And, when exposed to a slightly different kind of input, it fails to deliver accordingly. This is overfitting.

**4. How to prevent overfitting?**

**Tip:** This is one of the best Data Science interview questions for freshers** **that covers** **theoretical concepts**.**

The first thing we can do is to get more data. If that is not possible, we can opt for several techniques like cross-validation, early stopping, ensembling, regularisation, etc.

**5. Why do we need activation functions?**

Activation functions are used to convert the output of a particular layer of the model into a more readable and a constant form so that the subsequent layers of the model can read it.

**6. What are the differences between Supervised and Unsupervised Learning?**

**Tip:** This is the type of Data Science interview question that everyone should know.

In Supervised Learning, the model is trained over a set of inputs and outputs. In contrast, in Unsupervised Learning, the model is supposed to identify patterns in a given set of random data.

**7. What is the “power” of a hypothesis test?**

**Tip:** This is one of the problematic theory **i**nterview questions in Data Science interview question**.**

The probability of not committing a type 2 error is called the power of a hypothesis test. Whenever we are performing a hypothesis test, we don’t want to have any error, or at least try to minimize the probability of either committing a type 1 or a type 2 error. The power of a hypothesis test is related to the type 2 error.

**8. Explain how you will handle missing values in our data**?

**Tip:** An interesting and tricky interview question for Data Science freshers**, **generally asked by interviewers.

The first thing that you can do if your record has very little real data, and most of it is missing, is to not include that record at all in your analysis. We must exclude it because it is so poor in quality that it is going to affect the analysis negatively. So, instead of making a record that has very little data, it’s just better to exclude it.

However, in case if you are collecting customer data, they miss out on some information, either because they made a manual mistake or they did not answer. You can reach out to them again, and you can ask them again and try to gather the data if that is a possibility.

Otherwise, you can try to represent the values and populate the missing data by just making educated guesses. This can be done by taking some averages or through regression, or you can come up with your way how you want to impute the missing value.

**9. Lay out the differences between a histogram and a bar graph?**

**Tip:** This is one of the visualizations questions asked in Data Science interviews.

The bar graph is used for discrete data while the histogram is used for continuous data. In a bar graph, there is a space between the bars, while there is no space between bars in a histogram. That’s because the histogram is a continuous scale. Also, the order of the bars can be changed and sorted according to the requirements of the situation in a bar graph. In contrast, this is not a possibility in the histogram.

**10. How do you decide the value of K for K-Means clustering?**

**Tip:** This is a fundamental** **Data Science interview question**.**

Although it entirely depends on the need of the situation. To start with decent accuracy, we take k = the number of clusters in the data and try to play around that K in a quest to get better accuracy.

## 11. When do we confidently declare that a time series has become stationary?

**Tip**: This is one of the important time-series questions asked Data Science interview questions.

We observe the variance and mean of the series. If both of them are constant over time, we declare it to be stationary.

**12. What are the different types of joins in data queries?**

Interviewers can also ask a bit of analysis type of questions for Data Science interviews.

*There are 3 major types-*

- Inner Join
- Left Outer Join
- Right Outer Join

**13. Elaborate on the steps in the making of a decision tree**?

**Tip:*** *This is one of the top interview questions for Data Science freshers-

- Take the whole data set as input.
- Try to split the data into different clusters and find the best split combination that has maximum separation of the classes.
- Use this split setting on the input data.
- Now, on the divided data, redo steps 1 to 3.
- Whenever you observe any stopping criteria, terminate the algorithm.
- If you went too far doing splits, clean up the tree.

**14. When do we opt for resampling?**

**Tip:** This is another critical** **Data Science interview question.

Resampling is preferred during any of the following situations:

- When we need to pre-estimate the accuracy of an algorithm by taking a small subset of data or draw conclusions from it.
- During performance significance tests, if we need to substitute labels of the data.
- For validation of different models on subsets of a larger data.

**Conclusion**

Although this is not the ultimate set of** **Data Science interview questions, we have covered the important questions from almost every topic. Hence, it is highly recommended that you read the job description carefully and according to the employer’s vision prepare for the specific topic in depth.

It is also important for any candidate appearing for Data Science interviews to have an elegant resume. The resume should include all your necessary experiences, presented in an impactful form. Candidates should refrain from adding any skills and keywords that they are not confident about.

You should keep practicing Data Science techniques and operations on different platforms like Kaggle, Github, etc. This is to make sure that you are up to date with the recent trends in the industry and feel confident about any relevant questions posed to you during the interview.

Interested to learn more about Data Science? Our Full Stack Data Science Program can help you understand and learn all the important aspects of the domain, as well as provide you with a hands-on learning experience.

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