If you’re gearing up for a data science or analytics interview at Revolut, you’re likely diving into a world of exciting challenges and cutting-edge technologies. To help you prepare and ace your interview, let’s explore some key questions you might encounter, along with detailed answers to guide you through.
Table of Contents
Technical Interview Questions
Question: What is reinforcement learning?
Answer: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. It learns through trial and error, receiving feedback in the form of rewards or penalties for its actions. The goal is for the agent to learn the optimal strategy to maximize cumulative rewards over time.
Question: What’s the difference between stochastic gradient descent and gradient descent?
Answer: Gradient Descent: Updates model weights by computing the gradient of the loss function across the entire training set. It’s more precise but slower, especially with large datasets.
Stochastic Gradient Descent (SGD): Updates weights using the gradient from a single sample or small batch. It’s faster and can handle large datasets but introduces more noise, potentially causing more fluctuation in the loss minimization path.
Question: Describe PCA.
Answer: Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction while preserving as much of the variance in the dataset as possible. It achieves this by:
Identifying the directions (principal components) that maximize the variance in the data.
Projecting the original data onto these new dimensions with reduced features. PCA helps in simplifying the complexity of high-dimensional data while retaining trends and patterns.
Question: Explain Bagging vs Boosting.
Answer:
Bagging (Bootstrap Aggregating): Aims to improve the stability and accuracy of machine learning algorithms through ensemble methods. It involves training multiple models (typically of the same type) in parallel on random subsets of the original dataset (with replacement) and then combining their predictions. Bagging reduces variance and helps to avoid overfitting.
Boosting: A sequential ensemble technique that combines multiple weak learners to form a strong learner. Each model is trained to correct the errors made by the previous ones. The models are weighted based on their accuracy, and predictions are made by aggregating the results. Boosting focuses on reducing bias as well as variance, aiming to improve prediction accuracy.
Question: What is EDA?
Answer: EDA stands for Exploratory Data Analysis. It is an approach used in data analysis to summarize the main characteristics of a dataset, often with visual methods. EDA helps in understanding the data’s structure, identifying patterns, detecting anomalies, and formulating initial hypotheses. This process typically involves techniques such as summary statistics, data visualization, and data cleaning to prepare for further analysis.
Question: What is collaborative filtering?
Answer: Collaborative filtering is a technique used in recommendation systems to predict the preferences of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption is that if users A and B have agreed on how they rate items 1 and 2, they will likely have a similar opinion on item 3. It works by building a matrix of user-item interactions and applying algorithms to fill in missing values, suggesting items a user might like based on similarities with other users.
Question: Difference between Random Forest and Gradient Boosting?
Answer:
Random Forest:
- An ensemble of decision trees, typically trained with the bagging method.
- Trees are built in parallel and aim to reduce variance.
- Each tree votes and the majority vote decides the final prediction.
- Works well with a mix of numerical and categorical features and is less prone to overfitting.
Gradient Boosting:
- An ensemble technique that builds trees sequentially, with each tree trying to correct the errors of the previous one.
- Focuses on reducing both bias and variance by combining weak learners with a strong learner.
- Weights are assigned to all incorrect predictions, and the algorithm tries to improve them in the next tree.
- Can be more sensitive to overfitting if not properly tuned and generally requires more parameter tuning than Random Forest.
Question: What are Lasso and Ridge?
Answer:
Ridge Regression (L2 regularization): Adds a penalty equal to the square of the magnitude of coefficients to the loss function. This method aims to shrink the coefficients to prevent overfitting and improve model generalization. It’s particularly useful when dealing with multicollinearity or when the number of predictors exceeds the number of observations.
Lasso Regression (L1 regularization): Incorporates a penalty equal to the absolute value of the magnitude of coefficients. Beyond shrinking coefficients, Lasso can reduce the number of variables upon which the given solution is dependent. Thus, it can be used for variable selection and to produce models with fewer parameters for simpler interpretations.
Question: What is the difference between mean and median?
Answer:
Mean:
- Calculated by summing all values and dividing by the number of values.
- Sensitive to outliers, as extreme values greatly impact its value.
- Describes the average or central value of a dataset with a roughly symmetric distribution.
Median:
- Represents the middle value in a sorted list of numbers.
- Less affected by outliers compared to the mean.
- Useful for skewed distributions or datasets with extreme values, providing a robust measure of central tendency.
Question: Why is the mean better/preferred than the median?
Answer: The mean is often preferred over the median for several reasons:
- Efficiency in Estimation: The mean uses all the data points in its calculation, providing a more precise estimate of the average value.
- Statistical Properties: The mean is the best estimator of the population mean in the case of a normal distribution, and it has desirable statistical properties.
- More Information: It incorporates information from every data point, making it more sensitive to changes in the dataset.
Question: What are the different Bayesian methods used in data science?
Answer: Here are different Bayesian methods used in data science:
- Bayesian Inference
- Bayesian Networks
- Markov Chain Monte Carlo (MCMC)
- Hierarchical Bayesian Models
- Bayesian Optimization
ML and Python Interview Questions
Question: What is the difference between supervised and unsupervised learning?
Answer:
- Supervised Learning: Involves learning from labeled data where the model learns to map input data to the correct output.
- Unsupervised Learning: Involves learning from unlabeled data where the model discovers patterns and structures on its own.
Question: Explain the bias-variance tradeoff.
Answer:
- Bias: Error from erroneous assumptions in the learning algorithm, leading to underfitting.
- Variance: Error from sensitivity to fluctuations in the training data, leading to overfitting. The tradeoff refers to the balance needed between the two to achieve optimal model performance.
Question: What is cross-validation and why is it important?
Answer: Cross-validation is a technique used to assess how well a model will generalize to an independent dataset. It involves splitting the data into subsets for training and testing multiple times to get a more reliable estimate of the model’s performance.
Question: Describe the Random Forest algorithm.
Answer: Random Forest is an ensemble learning method that builds multiple decision trees during training and outputs the mode of the classes as the prediction. It combines bagging with feature randomness to improve performance and reduce overfitting.
Question: What are decorators in Python?
Answer: Decorators are functions that modify the behavior of other functions. They are often used to add functionality to existing functions without changing their code.
Question: Explain the difference between a list and a tuple in Python.
Answer: Lists are mutable, meaning their elements can be changed after creation. Tuples are immutable, meaning once they are created, their elements cannot be changed.
Question: How does Python handle memory management?
Answer: Python uses a private heap space to manage memory. Objects and data structures are allocated on this heap, and Python’s memory manager handles the allocation and deallocation of memory.
Question: What is a generator in Python?
Answer: A generator in Python is a function that allows you to generate a sequence of values over time. It uses the yield keyword to return values one at a time, allowing for efficient memory usage and lazy evaluation.
Interview Topics to Prepare
- Statistics, machine learning
- General Python questions
- Typical DS questions
- General software development/programming questions.
Other Technical Interview Questions
Que: How does gradient decent work, tell me step by step algorithm.
Que: Tell me the formula for L1 and L2 regularization.
Que: Simple task in Python
Que: The home task challenge was about SQL, data insights, and classification.
Que: What is the Wilcoxon test and how it works?
Que: Is there a “switch” operator in Python?
Que: What is the Mann–Whitney U test and how does it work?
Que: Basic DFS in a binary tree
Que: How to find out if a sample has a normal distribution?
Que: How can you escape/exit a Python for-loop?
Que: Describe your experience with the curse of dimensionality. How does this phenomena affect neural networks?
Que: What’s happening under the hood of the LightGBM package? Describe the algorithm.
Que: Dynamic programming challenge to find a combination of sums
Que: Give examples of neural networks you designed recently in your job.
Que: Which ML regularisation techniques do you know?
Que: Do you know what cooperative filtering is?
Que: Probability problem that could be solved with Bayes theorem
Behavioral Interview Questions
Que: Explain your background.
Que: What do you know about Revolut?
Que: Why do you want to work here?
Que: What are your Salary expectations?
Que: Which project you are proud of and why?
Conclusion
Preparation is key to success in any interview, especially in the dynamic field of data science and analytics. By familiarizing yourself with these questions and crafting thoughtful answers, you’ll be well-equipped to tackle the challenges of the interview process at Revolut.
Remember to not only focus on memorizing answers but also on understanding the underlying concepts. This will not only impress your interviewers but also help you excel in your future role.
Best of luck on your journey to becoming a data science and analytics expert at Revolut!
