In today’s data-driven world, companies like Walmart are at the forefront of harnessing the power of data science and analytics to drive business decisions. For aspiring data scientists and analysts looking to break into the industry or advance their careers, understanding the interview process is crucial. Walmart, a global retail giant, is known for its innovative use of data analytics to enhance customer experience, optimize operations, and make informed strategic choices. In this blog, we’ll delve into some common data science and analytics interview questions and answers specific to Walmart, offering valuable insights for those preparing for interviews at this renowned company.
Table of Contents
Technical Interview Questions
Question: What is R-squared?
Answer: R-squared (R²) is a statistical measure that indicates the proportion of the variance in the dependent variable that is explained by the independent variable(s) in a regression model. It ranges from 0 to 1, where 0 means no explanatory power and 1 means perfect explanatory power.
Question: Explain the difference between INNER JOIN, LEFT JOIN, and RIGHT JOIN in SQL?
Answer:
- INNER JOIN: Returns rows when there is at least one match in both tables. If there is no match, the rows are not returned.
- LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table, and the matched rows from the right table. If there is no match, the result is NULL on the right side.
- RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table, and the matched rows from the left table. If there is no match, the result is NULL on the left side.
Question: What are Optimization algorithms?
Answer: Optimization algorithms are mathematical techniques used to find the minimum or maximum value of a function. In machine learning, they are employed to minimize the cost function, which measures the difference between the model’s prediction and the actual data. Common examples include Gradient Descent, Stochastic Gradient Descent, and Adam. These algorithms iteratively adjust the parameters of the model to improve its accuracy.
Question: Difference between Inter-Cluster vs. Intra-Cluster?
Answer:
- Inter-Cluster: Refers to the distance or differences between clusters. In clustering algorithms, optimizing inter-cluster distance often involves maximizing it, so that clusters are as distinct from each other as possible.
- Intra-Cluster: Refers to the distance or differences within a single cluster. Optimizing intra-cluster distance typically involves minimizing it, ensuring that the elements within a cluster are as similar to each other as possible.
Question: What is the difference between Ridge and Lasso?
Answer:
Ridge Regression:
- Adds a penalty term equal to the square of the magnitude of coefficients.
- Helps to shrink the coefficients towards zero, but rarely makes them exactly zero.
- Useful when all features are potentially relevant.
Lasso Regression:
- Adds a penalty term equal to the absolute value of the magnitude of coefficients.
- Can shrink some coefficients to exactly zero, effectively performing feature selection.
- Useful when there are many irrelevant features and you want a sparse model.
Question: What is a Random forest?
Answer: Random Forest is an ensemble learning method used for both classification and regression tasks. It creates a “forest” of decision trees during training. Each tree is trained on a random subset of the data and a random subset of the features. When making predictions, it aggregates the predictions of all the individual trees to make a final prediction. This helps to improve the model’s accuracy and reduce overfitting compared to a single decision tree.
Question: What is the ROC curve?
Answer: The ROC curve, or Receiver Operating Characteristic curve, is a graphical representation used to evaluate the performance of a binary classification model. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings. The curve illustrates the trade-off between sensitivity (or TPR) and specificity (1 – FPR). The area under the ROC curve (AUC) provides a single measure of overall model performance across all classification thresholds, where a higher AUC indicates a model with better discrimination ability between the positive and negative classes.
Question: What is a confusion matrix?
Answer: A confusion matrix is a tool that summarizes the performance of a classification algorithm by displaying the number of correct and incorrect predictions broken down by each class. It shows true positives, false positives, true negatives, and false negatives. This enables the calculation of metrics like accuracy, precision, and recall to assess model performance.
Question: What is Bagging?
Answer: Bagging, or Bootstrap Aggregating, is an ensemble machine learning technique used to improve the stability and accuracy of machine learning algorithms. It involves creating multiple versions of a predictor model by training each version on a random subset of the training data, then aggregating their predictions (e.g., by voting for classification or averaging for regression) to form a final prediction. This approach reduces variance and helps to avoid overfitting, making the model more robust.
Question: What is Boosting?
Answer: Boosting is a machine learning ensemble technique that improves the performance of weak learners by sequentially training models, focusing on the mistakes of previous models. It assigns higher weights to misclassified instances in each iteration, leading subsequent models to pay more attention to these instances. The final prediction is made by combining the predictions of all models. Boosting reduces bias and variance, resulting in a more accurate and robust model.
Question: How to handle missing data?
Answer: To handle missing data, you can delete rows or columns with missing values, replace them with mean/median/mode, or use advanced techniques like multiple imputation or KNN imputation. Domain-specific methods based on context or domain knowledge can also be employed for better handling. The choice of method depends on the dataset, the amount of missing data, and the specific problem being addressed.
Question: What is the Radix Sort?
Answer: Radix Sort is a non-comparative sorting algorithm that sorts numbers by processing individual digits. It sorts numbers by grouping digits into buckets based on their values, starting from the least significant digit (LSB) to the most significant digit (MSB) or vice versa. Radix Sort can be used for integers or strings of characters, and its time complexity is O(nd), where n is the number of elements and d is the number of digits in the largest number.
Question: What is a CNN?
Answer: A CNN, or Convolutional Neural Network, is a deep learning model mainly used for image processing and classification. Its specialized architecture includes convolutional layers that learn hierarchical patterns in data, such as images. CNNs are effective for tasks like image recognition, object detection, and natural language processing.
Question: Describe what is DBSCAN algorithm.
Answer: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that identifies clusters of varying shapes and sizes in a dataset. It groups points if they are closely packed, based on two parameters: epsilon (ε) and minPts. Epsilon defines the radius of the neighborhood around each point, and minPts is the minimum number of points required to form a dense region. Points in high-density regions become part of a cluster, while points in low-density regions are considered noise.
Question: Difference between supervised and unsupervised learning?
Answer:
Supervised Learning:
- Uses labeled data, where the input features are paired with corresponding target labels.
- The goal is to learn a mapping from input to output by training on known examples.
- Algorithms include regression for continuous outputs and classification for discrete outputs.
- Focuses on predicting or estimating outcomes based on input features.
Unsupervised Learning:
- Deals with unlabeled data, where there are no predefined target labels.
- The goal is to find hidden patterns or structures in the data.
- Algorithms include clustering to group similar data points and dimensionality reduction to simplify data.
- Focuses on discovering the inherent structure of the data without explicit guidance.
Question: Explain SQL window functions.
Answer: SQL window functions allow calculations to be performed across a set of rows related to the current row, without using complex joins or subqueries. They are specified with an OVER clause and can calculate values like running totals, rankings, and moving averages. Common functions include ROW_NUMBER(), RANK(), SUM(), AVG(), and LAG()/LEAD() for accessing previous or next row values within the window.
Question: What is F distribution?
Answer: The F distribution is a probability distribution used in statistics, particularly for testing variances between groups in ANOVA. It has two parameters: degrees of freedom for the numerator (related to variability between groups) and denominator (related to variability within groups). A high F value indicates significant differences in group means, with the F statistic calculated as the ratio of between-group variance to within-group variance.
Question: What is cross entropy?
Answer: Cross-entropy is a measure used in classification tasks to quantify the difference between predicted and true probability distributions. In binary classification, it penalizes confident wrong predictions more. In multi-class scenarios, it measures the average bits needed to encode the true class, with lower values indicating better model performance.
Question: Difference between Gradient Boosting and Random Forest?
Answer:
Gradient Boosting:
- Builds sequential trees where each subsequent tree corrects the errors of the previous ones.
- Focuses on reducing errors by optimizing the loss function gradient during training.
- Typically slower to train but often yields higher accuracy, especially with smaller datasets.
- Prone to overfitting if hyperparameters are not tuned properly.
Random Forest:
- Construct multiple independent decision trees in parallel and combine their predictions.
- Uses random subsets of features and data points to reduce correlation among trees.
- Generally faster to train and less prone to overfitting due to the averaging effect.
- Works well with large datasets and is robust to outliers and noisy data.
Question: Assumptions of linear regression?
Answer: Assumptions of linear regression include:
- Linearity: The relationship between variables should be linear.
- Independence: Residuals should be independent of each other.
- Homoscedasticity: Residuals should have constant variance.
- Normality: Residuals should be normally distributed around zero. These assumptions ensure the validity and reliability of the regression model’s estimates and predictions.
Question: What are type I and II errors?
Type I Error:
- Also known as a “false positive.”
- Occurs when the null hypothesis is rejected when it is true.
- It indicates that the test incorrectly concludes there is an effect or difference when there isn’t one.
Type II Error:
- Also known as a “false negative.”
- Occurs when the null hypothesis is not rejected when it is false.
- It indicates that the test fails to detect an effect or difference that truly exists.
Question: What is the relationship between sample size and margin of error?
Answer: The relationship between sample size and margin of error is inversely proportional: as the sample size increases, the margin of error decreases. This means that larger sample sizes generally lead to more precise estimates with smaller margins of error. Conversely, smaller sample sizes tend to produce less precise estimates with larger margins of error. The margin of error quantifies the level of uncertainty in an estimate, so larger samples provide more confidence in the accuracy of the results.
Technical Interview Topics
- Hackkerrank SQL (difficult) Questions
- Machine learning questions (intermediate)
- SQL Medium
- Time series forecasting questions
- Questions based on Machine learning algorithms
- Questions on Lists, Dictionaries, and Sorting questions.
- SQL question related to a customer transaction log table.
- Basic probability questions.
- Data science question
- Regression
- Reinforcement learning
Other Technical Questions
Que: How did you describe the SVM model?
Que: Find the longest substring without repeated characters
Que: How many years of experience do you have in machine learning?
Que: What is your understanding of data science
Que: How to pick the most important features in data?
Conclusion
Preparing for a data science and analytics interview at Walmart requires a deep understanding of the company’s industry challenges and data-driven initiatives. By familiarizing yourself with these common interview questions and crafting thoughtful responses, you can showcase your expertise and problem-solving skills. Walmart continues to innovate in the realm of data analytics, offering exciting opportunities for professionals to make a significant impact. As you embark on your interview journey, remember to demonstrate your passion for data-driven decision-making and your ability to translate complex data into actionable insights that drive business success.
Best of luck with your Walmart data science and analytics interview!
