In today’s digital age, data has become the lifeblood of decision-making in businesses across industries. As companies strive to harness the power of data, roles in data science and analytics have become increasingly prominent. NatWest Group, a leading financial institution, is no exception, seeking talented individuals adept at leveraging data to drive insights and innovation.
Are you gearing up for a data science or analytics interview at NatWest Group? Fear not, as we delve into some common interview questions and provide insightful answers to help you prepare effectively.
Table of Contents
Technical Interview Questions
Question: Difference between knn and kmeans?
Answer: K-Nearest Neighbors (KNN) is a supervised learning algorithm that predicts the class of a data point by considering the majority class among its k nearest neighbors. On the other hand, K-Means is an unsupervised learning algorithm that partitions data into k clusters by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the mean of the data points assigned to each cluster.
Question: What is the fundamental difference between k-means clustering and KNN?
Answer: The fundamental difference between K-Means clustering and K-Nearest Neighbors (KNN) lies in their purpose and approach. K-Means is an unsupervised clustering algorithm that partitions data into distinct groups based on similarity, while KNN is a supervised classification algorithm that predicts the class of a data point by considering the majority class among its nearest neighbors. K-Means aims to group similar data points into clusters, whereas KNN makes predictions based on the known labels of neighboring data points.
Question: Explain hypothesis testing.
Answer: Hypothesis testing is a statistical technique used to assess the validity of claims about a population parameter based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha), selecting a significance level (alpha), calculating a test statistic from the sample data, and comparing it to a critical value or p-value to determine whether to reject or fail to reject the null hypothesis. This process helps researchers make conclusions and decisions based on the available evidence.
Question: What is PCA?
Answer: PCA, or Principal Component Analysis, is a statistical method used for dimensionality reduction. It identifies the most important features in a dataset and transforms them into a lower-dimensional space while retaining as much variance as possible. This technique is widely used in data preprocessing, visualization, and feature extraction to simplify complex datasets and uncover underlying patterns.
Statistics Interview Questions
Question: Can you explain the difference between population and sample?
Answer: “Population refers to the entire group of individuals or items that we are interested in studying, while a sample is a subset of the population that we observe and collect data from. For example, if we’re studying the income levels of customers at NatWest, the population would be all customers, while a sample might be a randomly selected group of 500 customers.”
Question: What is the central limit theorem, and why is it important?
Answer: “The central limit theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. This is important because it allows us to make inferences about population parameters based on sample data, even when we don’t know the population distribution.”
Question: How would you determine if two variables are correlated?
Answer: “To determine if two variables are correlated, we typically calculate the correlation coefficient, such as Pearson’s correlation coefficient. This statistic measures the strength and direction of the linear relationship between two variables. A correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation and close to 0 indicates no correlation.”
Question: Explain the difference between descriptive and inferential statistics.
Answer: “Descriptive statistics are used to summarize and describe the features of a dataset, such as measures of central tendency (mean, median, mode) and dispersion (standard deviation, range). Inferential statistics, on the other hand, are used to make inferences and predictions about a population based on a sample of data. This involves hypothesis testing, estimating population parameters, and assessing the reliability of the results.”
Question: What is hypothesis testing, and can you walk me through the steps?
Answer: “Hypothesis testing is a statistical method used to make inferences about a population parameter based on sample data. The steps typically involve: stating the null hypothesis (H0) and alternative hypothesis (Ha), selecting a significance level (alpha), choosing an appropriate test statistic and distribution, calculating the test statistic from the sample data, and making a decision to either reject or fail to reject the null hypothesis based on the test statistic and the significance level.”
Question: How would you handle missing data in a dataset?
Answer: “Handling missing data requires careful consideration to avoid biasing results. Depending on the nature of the missing data, techniques such as mean imputation, regression imputation, or multiple imputation can be used to estimate missing values. It’s important to assess the reasons for missingness and the potential impact on the analysis before deciding on an imputation method.”
Question: Can you explain the concept of p-value?
Answer: “The p-value is the probability of observing a test statistic as extreme as, or more extreme than, the one calculated from the sample data, assuming that the null hypothesis is true. It measures the strength of evidence against the null hypothesis. A p-value less than the chosen significance level (usually 0.05) indicates that the observed result is statistically significant, leading to rejection of the null hypothesis.”
Basic Math understanding questions
Question: What is the difference between simple interest and compound interest?
Answer: “Simple interest is calculated only on the principal amount, while compound interest is calculated on both the principal amount and the accumulated interest from previous periods. Compound interest tends to grow faster over time compared to simple interest.”
Question: How would you calculate the percentage increase of a value from one period to another?
Answer: “To calculate the percentage increase, subtract the initial value from the final value, then divide the result by the initial value, and finally multiply by 100 to express it as a percentage. The formula is: ((Final Value – Initial Value) / Initial Value) * 100.”
Question: Explain the concept of probability.
Answer: “Probability is a measure of the likelihood of a particular event occurring. It is expressed as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty. The probability of an event is calculated by dividing the number of favorable outcomes by the total number of possible outcomes.”
Question: If a train travels at a speed of 60 miles per hour, how far will it travel in 2.5 hours?
Answer: “To find the distance traveled, we multiply the speed of the train by the time it travels. So, 60 miles per hour multiplied by 2.5 hours equals 150 miles.”
Question: What is the formula for finding the area of a rectangle?
Answer: “The formula for finding the area of a rectangle is length multiplied by width. So, if the length is represented by ‘L’ and width by ‘W’, the formula would be Area = L * W.”
Question: How would you calculate the mean (average) of a set of numbers?
Answer: “To calculate the mean, you sum up all the numbers in the set and then divide by the total count of numbers in the set. So, Mean = (Sum of all numbers) / (Total count of numbers).”
Question: Explain the difference between median and mode.
Answer: “The median of a set of numbers is the middle value when the numbers are arranged in ascending order. If there is an even number of values, the median is the average of the two middle numbers. The mode is the value that appears most frequently in the set.”
Machine Learning Interview Questions
Question: What is machine learning, and how does it differ from traditional programming?
Answer: “Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. In traditional programming, rules and instructions are explicitly defined by the programmer, whereas in machine learning, the model learns patterns and relationships from data.”
Question: What are the main types of machine learning?
Answer: “The main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model is trained on labeled data, where the correct output is provided. In unsupervised learning, the model learns patterns and structures from unlabeled data. Reinforcement learning involves training a model to make sequences of decisions in an environment to maximize rewards.”
Question: Can you explain the bias-variance tradeoff?
Answer: “The bias-variance tradeoff is a fundamental concept in machine learning that describes the balance between the error due to bias and the error due to variance in a model. Bias refers to the error introduced by overly simplistic assumptions in the model, while variance refers to the error introduced by the model’s sensitivity to fluctuations in the training data. A high-bias model tends to underfit the data, while a high-variance model tends to overfit the data. Finding the right balance is crucial for building a model that generalizes well to unseen data.”
Question: What evaluation metrics would you use to assess the performance of a binary classification model?
Answer: “For a binary classification model, common evaluation metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (ROC AUC). Accuracy measures the overall correctness of predictions, precision measures the proportion of true positive predictions among all positive predictions, recall measures the proportion of true positives that were correctly identified, the F1 score is the harmonic mean of precision and recall, and ROC AUC measures the model’s ability to discriminate between positive and negative classes.”
Question: Explain the concept of overfitting and how you can prevent it.
Answer: “Overfitting occurs when a model learns to capture noise and random fluctuations in the training data, resulting in poor generalization to unseen data. To prevent overfitting, techniques such as cross-validation, regularization, and feature selection can be used. Cross-validation helps assess the model’s performance on unseen data by splitting the dataset into training and validation sets. Regularization techniques, such as L1 and L2 regularization, penalize overly complex models by adding a penalty term to the loss function. Feature selection involves selecting a subset of relevant features to reduce the complexity of the model.”
Question: What are some common algorithms used in supervised learning?
Answer: “Common algorithms used in supervised learning include linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and neural networks. Each algorithm has its strengths and weaknesses, and the choice of algorithm depends on the specific problem and the characteristics of the data.”
Question: How would you handle imbalanced datasets in machine learning?
Answer: “Imbalanced datasets occur when one class is significantly more prevalent than the others, leading to biased model performance. Techniques for handling imbalanced datasets include resampling methods such as oversampling the minority class or undersampling the majority class, using different evaluation metrics such as precision-recall curves instead of accuracy, and using algorithms that are robust to class imbalance, such as ensemble methods like random forests or gradient boosting.”
Conclusion
Preparing for a data science or analytics interview at NatWest Group requires a solid understanding of fundamental concepts, practical experience with data manipulation and modeling techniques, and the ability to articulate insights effectively. By mastering these key areas and embracing a data-driven mindset, you’ll be well-equipped to tackle the challenges and opportunities in the dynamic field of data science and analytics at NatWest Group.
