Data Science Interview Questions and Answers

The data science field is growing bigger by the day. As such, there are plenty of opportunities for those interested in pursuing a data scientist career. We won’t go into detail about it here, but if you are just starting out with data science, read about how to become a data scientist first.

If you already know the ropes, then it’s time to move on to data science interview questions so you can nab that dream role. After some general questions about what a data science interview is like, we list categorized interview questions and answers ranging from statistics to deep learning. Use these to aid your preparation.

How Do I Prepare for a Data Science Interview?

As you would for any other technical interview, make sure that you’ve got the basics down and can execute ideas in code. Of course, you should also present a good resume and be prepared to summarize past experiences. You should also be familiar with common interview questions. We'll get to those below.

On a more general note, you should also research the company and the specific role you’re applying for. You want to ask questions about the software and the company itself, as it serves to highlight your enthusiasm for the role. It may also be worth looking at reviews on Glassdoor to get a sense of the company and past employees’ experiences.

Are Data Science Interviews Tough?

Data science interviews are not necessarily tougher or easier than other interviews. This is a subjective question, so there is no unequivocal answer. If you've got a good grasp of the fundamentals, can thoroughly and clearly explain any projects you’ve worked on, and can execute technical concepts, you will do fine.

Do Data Scientists Have Coding Interviews?

Yes, you will likely be asked to code during a data science interview. However, the chances are lower than what you might expect for a typical software development role.

Usually, the coding questions relate to data manipulation or SQL knowledge, but you may also face questions related to algorithms, programming practices, and data structures. Data scientist interviews for roles at tech firms and those that focus on machine learning tend to involve coding questions. Some applicants may have a portfolio to showcase practical uses for their skills.

General Data Science & Process

These are some of the most common data science interview questions. They'll help a hiring manager evaluate general knowledge first.

1. What are the steps involved in an analytics project?

The following are the numerous steps involved in an analytics project:

• Understanding the Business Problem: Defining goals and success metrics.
• Data Exploration: Exploring the data and understanding it.
• Data Preparation: Preparing the data for modeling by detecting outlier values, transforming variables, treating missing values, etc.
• Modeling: Running the model and analyzing the result for making appropriate changes (an iterative step).
• Validation: Validating the model using a new dataset.
• Implementation: Implementing the model and tracking the result for performance analysis.

2. Between Python and R, which one would you pick for text analytics, and why?

For text analytics, Python generally gains the upper hand over R for the following reasons:

• The Pandas library in Python offers easy-to-use data structures as well as high-performance data analysis tools.
• Python has a faster performance for all types of text analytics.

Learn more about R vs Python here.

3. What skills are important to become a Data Scientist?

The skills required to become a certified Data Scientist include:

1. Knowledge of built-in data types including lists, tuples, sets, and related.
2. Expertise in N-dimensional NumPy Arrays.
3. Ability to apply Pandas Dataframes.
4. Strong hold over performance in element-wise vectors.
5. Knowledge of matrix operations on NumPy arrays.

4. What are the skills a Data Scientist requires, with respect to Python data analysis?

The skills required as a Data Scientist that would help in using Python for data analysis purposes are:

• Understanding Pandas Dataframes, Scikit-learn, and N-dimensional NumPy Arrays.
• Knowing how to apply element-wise vector and matrix operations on NumPy arrays.
• Understanding built-in data types, including tuples, sets, dictionaries, and so on.
• Knowing Anaconda distribution and the Conda package manager.
• Writing efficient list comprehensions, small, clean functions, and avoiding traditional for loops.
• Knowledge of Python script and optimizing bottlenecks.

Statistics & Probability

These data science interview questions are key for data analytics. It's an essential part of interview prep.

5. What is Selection Bias and what are the various types?

Selection bias is a distortion of statistical analysis that results from the sample collecting method. It occurs when the researcher decides who is going to be studied, rather than selecting participants randomly. When selection bias is not taken into account, conclusions made by a study might not be accurate.

The various types of selection bias include:

• Sampling Bias: A systematic error resulting due to a non-random sample of a populace causing certain members to be less likely to be included than others.
• Time Interval: A trial might end at an extreme value (usually due to ethical reasons), but the extreme value is most likely to be reached by the variable with the most variance.
• Data: Results when specific data subsets are selected for supporting a conclusion or rejection of bad data arbitrarily.
• Attrition: Caused due to loss of participants, discounting trial subjects, or tests that didn’t run to completion.

6. What is the goal of A/B Testing?

A/B Testing is a statistical hypothesis test meant for a randomized experiment with two variables, A and B. The goal is to maximize the likelihood of an outcome of interest by identifying any changes to a webpage. It is a highly reliable method for finding out the best online marketing and promotional strategies.

7. What are linear regression and logistic regression?

Linear regression is a statistical technique in which the score of a variable Y (criterion variable) is predicted on the basis of the score of a second variable X (predictor variable).

Logistic regression (also known as the logit model) is a statistical technique for predicting a binary outcome from a linear combination of predictor variables.

8. What are outlier values and how do you treat them?

Outlier values are data points that differ significantly from other observations in the dataset. Not all extreme values are outliers, but they must be investigated.

There are two popular ways of treating outliers:

1. Transformation: Changing the value so that it can be brought within a range (e.g., Log transformation).
2. Removal: Simply removing the value if it is deemed an error or irrelevant.

9. What do you mean by cluster sampling and systematic sampling?

Cluster Sampling is used when studying a target population spread throughout a wide area. The population is divided into clusters (groups), and a sample of these clusters is chosen for study.

Systematic Sampling involves choosing elements from an ordered sampling frame. The list is advanced in a circular fashion so that once the end is reached, it progresses from the start again.

10. What are Eigenvectors and Eigenvalues?

Eigenvectors help in understanding linear transformations. They are the directions along which a particular linear transformation acts by compressing, flipping, or stretching.

Eigenvalues can be understood as the strength of the transformation in the direction of the eigenvectors.

Machine Learning Fundamentals

These focus on machine learning. Any data science applicant should have a solid understanding of the following. 

11. What are the differences between Supervised and Unsupervised Learning?

Supervised learning infers a function from labeled training data. The training data contains a set of training examples.

Unsupervised learning draws inferences from datasets containing input data without labeled responses.

12. Explain overfitting and underfitting.

Overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. This happens when a model is excessively complex (e.g., too many parameters relative to observations). An overfitted model overreacts to minor fluctuations in training data.

Underfitting occurs when a model cannot capture the underlying trend of the data (e.g., fitting a linear model to non-linear data). An underfit model under-reacts to fluctuations.

13. Can you compare the validation set with the test set?

A validation set is part of the training set used for parameter selection. It helps avoid overfitting the model being developed.

A test set is meant for evaluating the final performance of a trained machine learning model. It should never be used during the training phase.

14. What is the purpose of data cleaning in data analysis?

Data cleaning can take up to 80% of the time required for a data analysis task. However, it is vital because:

• Cleaning data from different sources helps transform it into a format that is easy to work with.
• Data cleaning increases the accuracy of a machine learning model.

15. How do you define the number of clusters in a clustering algorithm?

Generally, the Within Sum of Squares (WSS) is used. WSS is plotted for a range of cluster numbers. The resultant graph is known as the Elbow Curve.

The Elbow Curve contains a point where there are no significant decrements in WSS. This is known as the "bending point" and represents the optimal K in K–Means.

16. Explain Recommender Systems and state an application.

Recommender Systems are information filtering systems meant for predicting the preference or rating a user would give to a product.

Application: Amazon's "Customers who bought this also bought..." section uses recommender systems based on user search history and past orders.

17. What is Ensemble learning?

Ensemble learning is the process of combining diverse sets of individual models (learners) to improve the stability and predictive power of the final model.

18. What are the different kinds of Ensemble learning?

1. Bagging: Implements simple learners on small populations and takes the mean for estimation.
2. Boosting: Adjusts the weight of an observation based on the previous classification.

19. What are the various Machine Learning Libraries and their benefits?

• Numpy: Scientific computation.
• Statsmodels: Time-series analysis.
• Pandas: Tubular data analysis.
• Scikit-learn: Data modeling and pre-processing.
• TensorFlow: Deep learning.
• Regular Expressions: Text processing.
• Pytorch: Deep learning.
• NLTK: Text processing.

Deep Learning & Neural Networks

20. What is Deep Learning?

Deep Learning is a paradigm of machine learning that resembles the functioning of the human brain. It is based on Artificial Neural Networks (ANN). It has gained exposure recently due to the increase in data generation and the growth in hardware resources (GPUs).

21. What are the differences between Deep Learning and Machine Learning?

22. What is Gradient Descent?

Gradient Descent is a minimization algorithm. It is a mathematical function that "walks" down to the bottom of a valley to find the minimum value of a function. The gradient measures the change in all weights with respect to the change in error.

23. What is the difference between Batch and Stochastic Gradient Descent?

24. How does backpropagation work?

Backpropagation is a training algorithm for multilayer neural networks. The error is moved from the end of the network back to all weights inside the network, allowing for efficient computation of the gradient.

[Image of backpropagation algorithm flowchart]

25. What is an Epoch, Batch, and Iteration?

• Epoch: One full iteration over the entire dataset.
• Batch: A broken-down collection of the dataset used when the full dataset cannot be passed into the neural network at once.
• Iteration: A classification of data into groups applied within an epoch.

Example: With 50,000 images and a batch size of 100, one Epoch will run 500 iterations.

26. What are hyperparameters?

Hyperparameters are parameters whose values are set before the learning process begins (e.g., Learning Rate, Number of Epochs, Hidden Units). They determine the network structure and training requirements.

27. What is the Cost Function?

The Cost Function evaluates how good the model’s performance is. It measures the errors and losses in the output layer during backpropagation.

28. Why is TensorFlow considered important in Data Science?

TensorFlow provides support for C++ and Python, allowing for faster compilation and completion compared to other libraries. It supports both CPU and GPU for faster inputs, editing, and data analysis.

29. What is the Computational Graph?

A Computational Graph (or DataFlow Graph) in TensorFlow is a network of nodes where each node represents a mathematical operation. The edges connecting these nodes are called tensors.

30. What are Tensors?

Tensors are mathematical objects that represent collections of higher dimensions of data inputs (alphabets, numerals, rank) fed into the neural network.

31. What is Dropout?

Dropout is a tool used to prevent overfitting. It randomly "drops out" (ignores) hidden and visible units of a network (up to 20%) during training.

32. What is Batch Normalization?

Batch normalization improves the performance and stability of a neural network by normalizing inputs in each layer so the mean output activation remains 0 with a standard deviation of 1.

33. What is an Activation Function?

An Activation Function introduces non-linearity into the neural network. Without it, the network could only perform linear functions. It decides whether a neuron should be activated or not.

34. What are Vanishing Gradients?

A Vanishing Gradient occurs when the slope is too small during the training of Recurrent Neural Networks (RNNs). This results in poor performance, low accuracy, and long-term training processes. The visualization above shows this.

35. What are Exploding Gradients?

An Exploding Gradient occurs when errors grow at an exponential rate during RNN training. This causes an overflow and results in NaN values.

36. What is an RNN?

Recurrent Neural Networks (RNN) are artificial neural networks designed for sequential data, such as stock markets or time series. They are capable of learning dependencies over time.

37. What is LSTM?

LSTM (Long Short Term Memory) is a type of RNN capable of learning long-term dependencies and recalling information for a longer period. It uses a 3-step process:

1. Decide what to remember/forget.
2. Select cell state values to update.
3. Decide what to output.

38. What is Pooling in CNN?

Pooling is used to reduce the spatial dimensions of a CNN (Convolutional Neural Network). It helps downsample operations to reduce dimensionality and create pooled feature maps.

39. What are the different layers on CNN?

1. Convolutional Layer: Small picture windows go over the data.
2. ReLU Layer: Brings non-linearity to the network (converts negative pixels to zero).
3. Pooling Layer: Reduces the dimensionality of the feature map.
4. Fully Connected Layer: Recognizes and classifies the objects.

40. What is GAN?

GAN (Generative Adversarial Network) takes inputs from a noise vector and sends them to a Generator (which creates fake copies) and a Discriminator (which tries to identify unique vs. fake copies).

[Image of generative adversarial network architecture diagram]

41. What do you know about Autoencoders?

Autoencoders are learning networks used for transforming inputs into outputs with minimal error. They compress input into a lower-dimensional code and then reconstruct the output from this representation.

42. Explain the Boltzmann Machine.

A Boltzmann Machine uses a simple learning algorithm to discover complex regularities in training data. It is primarily used for optimizing the quantity and weights for a given problem.

43. What is a Normal Distribution?

A Normal Distribution (also known as the Gaussian distribution or Bell Curve) is a probability distribution that is symmetric about the mean. In a normal distribution, data near the mean are more frequent in occurrence than data far from the mean.

Key characteristics include:

• The Mean, Median, and Mode are all equal.
• 68% of the data falls within 1 standard deviation of the mean.
• 95% of the data falls within 2 standard deviations.
• 99.7% of the data falls within 3 standard deviations.

44. What is a Confidence Interval?

A Confidence Interval measures the degree of uncertainty or certainty in a sampling method. It is a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter.

Example: A 95% confidence interval does not mean there is a 95% probability that the parameter lies within the interval. It means that if you took 100 different samples and computed a confidence interval for each, approximately 95 of those intervals would contain the true population parameter.

45. What is Feature Selection and what are the main methods?

Feature Selection is the process of selecting a subset of relevant features (variables) for use in model construction. It helps simplify models, reduces training time, and avoids the "Curse of Dimensionality."

The three main methods are:

1. Filter Methods: Select features based on statistical scores (e.g., Chi-Square, Correlation Matrix) independent of the machine learning algorithm.
2. Wrapper Methods: Select a set of features and then assess their performance using a specific algorithm (e.g., Recursive Feature Elimination).
3. Embedded Methods: The algorithm itself decides which features are best during the training process (e.g., LASSO, Ridge Regression).

46. What is Regularization? Explain L1 vs L2.

Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. It discourages the model from learning overly complex patterns.

The two most common types are:

• L1 Regularization (Lasso): Adds the absolute value of the magnitude of coefficients as a penalty term. It can shrink coefficients to zero, effectively performing feature selection.
• L2 Regularization (Ridge): Adds the squared magnitude of coefficients as a penalty term. It shrinks coefficients toward zero but rarely reaches absolute zero.

47. What is a Confusion Matrix?

A Confusion Matrix is a table used to evaluate the performance of a classification model. It compares the actual target values with those predicted by the machine learning model. It is not just about accuracy; it breaks down the errors into four specific categories: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).

This matrix is essential because a model can have high accuracy but still be useless if it fails to detect the minority class (e.g., detecting fraud). From the confusion matrix, you can calculate other important metrics like Precision, Recall, and F1-Score.

48. Explain True Positive, True Negative, False Negative, and False Positive.

These terms describe the four possible outcomes in a binary classification problem:

• True Positive (TP): The model correctly predicts the positive class (e.g., The patient has the disease, and the model predicted "Disease").
• True Negative (TN): The model correctly predicts the negative class (e.g., The patient is healthy, and the model predicted "Healthy").
• False Positive (FP): The model incorrectly predicts the positive class (e.g., The patient is healthy, but the model predicted "Disease"). This is also known as a Type I Error.
• False Negative (FN): The model incorrectly predicts the negative class (e.g., The patient has the disease, but the model predicted "Healthy"). This is also known as a Type II Error.

49. What is the difference between a False Null Hypothesis and a True Null Hypothesis?

In statistical hypothesis testing, the Null Hypothesis (H0​) is the default assumption that there is no effect or no difference.

• True Null Hypothesis: This means that in reality, there is no effect (e.g., a new drug truly has no impact compared to a placebo). If you reject a True Null Hypothesis, you are making a Type I Error (False Positive).
• False Null Hypothesis: This means that in reality, there is an effect (e.g., the drug actually works). If you fail to reject a False Null Hypothesis, you are making a Type II Error (False Negative)—essentially missing a discovery that was there.

Future Trends in Data Science

Hiring managers often ask about trends to see if you keep up with the industry. Here are a few concepts to know:

50. What is Generative AI (GenAI)?

Generative AI refers to algorithms (such as LLMs like GPT-4) that can create new content, including text, code, images, and audio. Unlike traditional discriminative AI, which classifies data, GenAI produces new data instances based on learned patterns.

51. What is MLOps?

MLOps (Machine Learning Operations) is the practice of combining Machine Learning, DevOps, and Data Engineering. Its goal is to deploy and maintain ML models in production reliably and efficiently. It addresses issues like model drift, version control, and automated retraining.

52. Explain Explainable AI (XAI).

Explainable AI (XAI) refers to methods and techniques that allow the results of machine learning models to be understood by humans. As models (like Deep Neural Networks) become "black boxes," XAI is crucial for building trust, especially in regulated industries like finance and healthcare.

The Bottom Line

That completes the list of the top data science interview questions. This list is by no means exhaustive, and we urge you to do more study — particularly for data science technical interview questions.

Taking some of the best data science courses and tutorials will also help.