In 2012, Harvard Business Review named data science the sexiest job of the 21st century. But if you want to get a job as a data scientist, you'll need to go through a tough interview process.

During data science job interviews, the interviewer will likely ask questions from different data science topics such as statistics, programming, data analysis, data pre-processing, and modeling.

Your skills will be put to the test, and you need to prepare yourself if you want to get through the interview successfully.

In this article, I have compiled a list of common data science interview questions with tips on how you can answer them. I've also shared a list of resources that will help you learn more about the specific topic presented in each interview question.

# Data Science Interview Questions

## What is Logistic Regression? How Have You Used Logistic Regression Recently?

Logistic regression is a popular algorithm used to solve classification problems. In this question, you need to explain what logistic regression is, how it works, and give an example of a data science problem you solved by using logistic regression.

**Here are resources to help you get started crafting your response:**

- Logistic Regression: The good parts
- The Least Squares Regression Method – How to Find the Line of Best Fit

## Why do we Need Evaluation Metrics? What is a Confusion Matrix?

Machine learning models must be evaluated to check their performance. In this question, you need to explain how you can use the confusion matrix to evaluate the model's performance. You can further mention other metrics to evaluate regression and classification models.

**Here are resources to help you get started crafting your response:**

- 9 Key Machine Learning Algorithms Explained in Plain English
- How I used Deep Learning to classify medical images with Fast.ai

## How is Data Science Different from Traditional Application Programming?

A good way to answer this question is by using examples of how the program is created in both cases.

Traditional programming approach:

Data science approach:

**Here is a good resource to help you get started crafting your response:**

## Explain the Difference Between Supervised and Unsupervised Learning.

Supervised and unsupervised learning are two types of machine learning techniques. The best way to answer this question is by explaining their differences in terms of the kind of datasets you can use in each technique and examples of algorithms.

**Here is a good resource to help you get started crafting your response:**

- When to use different machine learning algorithms: a simple guide
- Want to know how deep learning works? Here's a quick guide

## What is a Decision Tree?

A decision tree is another supervised learning algorithm that you can use to solve regression or classification problems.

You should be able to explain how the decision tree algorithm learns from the data and the advantages and disadvantages of using a decision tree algorithm.

**Here are resources to help you get started crafting your response:**

- How to Use Tree-Based Algorithms in Machine Learning
- 9 Key Machine Learning Algorithms Explained in Plain English

## What is Cross-Validation?

The purpose of this question is to determine if you know any techniques used to assess the effectiveness of the machine learning model – for example, when you want to avoid overfitting.

When answering this question, you should explain any methods of cross-validation you have applied in any data science projects.

**Here are resources to help you get started crafting your response:**

## What is a Normal Distribution?

This term is commonly used when you're solving a data science problem. In this question, you can explain the meaning of normal distribution, its properties, and why it is important to check if your data is normally distributed.

**Here are resources to help you get started crafting your response:**

## What is a Random Forest Algorithm?

Random forest is one of the most popular machine learning algorithms. When answering this question, you should explain how the algorithm learns from the data and when you should use the random forest algorithm over other machine learning algorithms.

**Here are resources to help you get started crafting your response:**

- Random Forest Classifier Tutorial
- Dataset Splitting and Random Forest Algorithms
- Random Forest Algorithm Explained

## Explain Univariate, Bivariate, and Multivariate Analyses

These three types of analyses are used to summarize variables in the dataset and help you get some insights. You can also talk about how they're different and when you can apply them – just make sure to show some examples.

**Here are resources to help you get started crafting your response:**

- Univariate, Bivariate and Multivariate Analysis
- How to Select the Best Performing Linear Regression for Univariate Models

## How can we Handle Missing Data?

Some datasets may have missing data or values and can cause a problem when training machine learning models.

It is important to mention some techniques that can be used to handle missing data. You can also share your experience of how you handled missing data in your last data science project.

**Here are resources to help you get started crafting your response:**

- The Penalty of Missing Values in Data Science
- Feature Engineering and Feature Selection for Beginners
- Handling Missing Data Easily Explained

## What is the Benefit of Dimensionality Reduction?

Dimensionality reduction is a technique to reduce the number of features or variables in the dataset.

There are different advantages of dimensionality reduction you can explain when answering this question. You should explain why and when you need to apply this technique.

**Here are resources to help you get started crafting your response:**

- How to use dimensionality reduction
- Escaping the curse of dimensionality
- Pros and Cons of Dimensionality Reduction

## How can we deal with Outliers?

An outlier is a data point that deviates significantly from the rest. In this question, you can explain how one can identify outliers and different techniques used to deal with outliers.

**Here are resources to help you get started crafting your response:**

- What is an Outlier in Statistics?
- Three Ways to Deal with Outliers
- How to Remove Outliers from a Dataset

## What is Ensemble Learning?

In machine learning, ensemble learning is a process of using multiple algorithms to obtain better predictive performance than could be obtained from any one algorithm alone.

When answering this question, you can also share your experience the last time you implemented ensemble methods in a data science project.

**Here are resources to help you get started crafting your response:**

## Explain how Machine Learning is Different from Deep Learning

The best way to explain the difference between machine learning and deep learning is the way they solve problems.

You can go further by explaining some of the problems that can be solved by either machine learning or deep learning techniques.

**Here are resources to help you get started crafting your response:**

- A beginner's guide to Machine Learning and Deep Learning
- AI vs ML – What's the Difference between Artificial Intelligence and Machine Learning?
- Machine Learning Crash Course and Deep Learning Crash Course

## What are the Differences Between Overfitting and Underfitting?

The best way to explain the difference between overfitting and underfitting is not just with a definition but through examples.

You can also share your personal experience when faced with overfitting or underfitting problems in a data science project.

**Here are resources to help you get started crafting your response:**

- How to Handle Overfitting in Deep Learning Models
- How to Build Better Machine Learning Models
- Deep Learning with PyTorch Course

## What is Regularisation? Why is it Useful?

When answering this question, you can also go further by explaining the two common regularization techniques L1 norm and L2 norm.

**Here are resources to help you get started crafting your response:**

## What is Selection Bias?

It is not enough just to define Selection Bias. If possible you should explain different types of bias, their effects, and how to avoid them.

**Here are resources to help you get started crafting your response:**

## Can you Explain the Difference Between a Validation Set and a Test Set?

In this question, after explaining their differences, you can explain the advantage of having a validation set and a test set in a data science project.

**Here are resources to help you get started crafting your response:**

- Key Machine Learning Concepts Explained
- Difference between Test Sets and Validation Sets
- What to do when your training and testing data come from different distributions
- Machine Learning – Validation vs Testing

## What is the Difference Between Regression and Classification ML Techniques?

We all know that regression and classification are supervised learning and the only difference is their output. When you answer this question, you can mention a few algorithms that can be used to solve regression problems or classification problems. Also, try to share how their models are evaluated.

**Here are resources to help you get started crafting your response:**

- How to Build and Train Linear and Logistic Regression ML Models
- Regression vs Classification in Machine Learning
- Machine Learning Basics for Developers
- Classification and Regression in Machine Learning

## What are Artificial Neural Networks?

In this question don't just define Artificial Neural Networks but also explain their advantages and where you can use them.

**Here are resources to help you get started crafting your response:**

- Overview of Artificial Neural Networks and their Applications
- Deep Learning Neural Networks Explained in Plain English

## What Tools and Devices do you Plan to use in Your Role as a Data Scientist?

This question is straightforward but you should mention tools you have used before or you are planning to use in the future project.

You can also share your experience of how various tools help you implement data science projects successfully.

Keep in mind that you will use different tools for different projects. For example, some tools can be used for an NLP project and others for a Time-series project.

**Here are resources to help you get started crafting your response:**

## What is Natural Language Processing? State some Real-Life Examples of NLP.

You have to define Natural language processing in a simple way and how it can be used to solve business problems. Then share some real-life examples. If possible you can also share some of the NLP projects you have done or collaborate with others.

**Here are resources to help you get started crafting your response:**

- What is Natural Language Processing? A tutorial for beginners
- Learn Natural Language Processing with Python and TensorFlow
- What Every Developer Needs to Know about NLP
- Applications of NLP

## What is Normalisation? Difference between Normalisation and Standardization?

Normalization and standardization are techniques used to pre-process the data before applying machine learning algorithms.

The purpose of the question is to explain the differences between these two techniques and at what condition of the dataset you should apply one over another.

**Here are resources to help you get started crafting your response:**

- The Difference Between Normalization and Standardization
- Text Preprocessing for NLP and Machine Learning
- Feature Engineering and Feature Selection for Beginners
- Standardization vs Normalization – Feature Scaling
- Preprocessing for Deep Learning

## Final Thoughts on Data Science Interview Questions

Reviewing these common data science interview questions will actually boost your confidence during the interview.

Don't expect the interviewer to ask you all questions mentioned in this article. But most of the interview questions will come from the same topics.

For example, instead of asking "**Explain the difference between supervised and unsupervised learning**", the interviewer can ask you to “**Explain some supervised learning algorithms and how they learn from the data**”.

If you are interested in learning and reading more data science interview questions, take your time and read through these additional resources for inspiration.

And don't forget to practice your coding skills because some questions during the interview require you to code the solution.

I hope these data science interview questions will help you prepare for your interview and I wish you the best of luck in your data science career.

If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post!

You can also find me on Twitter @Davis_McDavid.

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