Preparing for a data science and analytics interview at Expedia Group means gearing up to demonstrate your technical prowess, problem-solving abilities, and passion for leveraging data to drive decisions. Expedia, being at the forefront of the travel industry, relies heavily on data to optimize user experience, forecast trends, and make strategic business decisions. Here, we’ll explore some potential interview questions and answers to help you prepare for your big day.
Understanding the Expedia Group Interview
Expedia’s interview process typically assesses both technical skills and cultural fit. For data science and analytics roles, expect questions ranging from statistical theory and machine learning to case studies and behavioral scenarios. Showcasing your ability to communicate complex analyses to non-technical stakeholders is also crucial.
Table of Contents
ML Interview Questions on SVM, Decision Tree, and Regression
Question: What is an SVM and how does it work?
Answer: An SVM is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best separates different classes in the feature space. The best hyperplane is the one that has the largest margin, which is the maximum distance between data points of both classes. SVMs are also capable of performing non-linear classification using the kernel trick, mapping their inputs into high-dimensional feature spaces.
Question: Can you explain the kernel trick in SVMs?
Answer: The kernel trick is a technique used by SVMs to handle non-linear data. It involves using a kernel function to transform the data into a higher-dimensional space where it is possible to find a linear separator or hyperplane. Common kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid. This allows SVMs to efficiently perform non-linear classification and regression without explicitly mapping data to higher dimensions.
Question: What are support vectors in SVM?
Answer: Support vectors are the data points that lie closest to the decision boundary (or hyperplane) in an SVM model. These points are critical since they are the ones that determine the position and orientation of the hyperplane. Essentially, support vectors are the elements of the training set that, if removed, would alter the positioning of the dividing hyperplane. Because they directly influence the decision boundary, they are key to the SVM’s classification decision.
Question: What is a Decision Tree and how is it used in classification and regression tasks?
Answer: A Decision Tree is a flowchart-like tree structure where internal nodes represent feature attributes, branches represent decision rules, and each leaf node represents the outcome. In classification tasks, it is used to identify the class to which a target variable belongs, while in regression tasks, it predicts a continuous quantity. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
Question: How do you prevent overfitting in Decision Trees?
Answer: Overfitting can be prevented in Decision Trees by:
- Pruning: Trimming down the branches of the tree that use features with low importance to reduce complexity.
- Setting a maximum depth for the tree.
- Minimum samples for a node split: Defining the minimum number of samples required to split an internal node.
- Minimum samples for a leaf node: Setting the minimum number of samples a leaf node must have.
- Using ensemble methods like Random Forests that combine multiple decision trees to improve performance and reduce overfitting risk.
Question: What is the difference between linear and logistic regression?
Answer: Linear regression is used for predicting a continuous outcome variable based on one or more predictor variables. It models the relationship between the dependent and independent variables by fitting a linear equation to observed data. Logistic regression, on the other hand, is used for binary classification tasks. It models the probability that a given input belongs to a particular category (0 or 1) by using a logistic function.
Question: How do you evaluate the performance of a regression model?
Answer: The performance of a regression model can be evaluated using several metrics:
- Mean Absolute Error (MAE): The average of the absolute errors between predicted and actual values.
- Mean Squared Error (MSE): The average of the squared differences between predicted and actual values.
- Root Mean Squared Error (RMSE): The square root of MSE, providing a measure of the differences between values predicted by the model and the values observed.
- R-squared (R²): Represents the proportion of the variance for the dependent variable that’s explained by the independent variables in the model.
SQL Interview Questions with Window function and Join
Question: What is a window function in SQL and how does it differ from an aggregate function?
Answer: A window function performs a calculation across a set of rows that are related to the current row in some way, defined by the OVER() clause. Unlike aggregate functions, window functions do not group the rows into a single output row; they allow the calculation to “move” with each row in the result set. This means each row can have a unique value based on the calculation applied over the window defined for that row.
Question: Can you explain the significance of the PARTITION BY clause in window functions?
Answer: The PARTITION BY clause is used within the OVER() clause of a window function to divide the result set into partitions or groups within which the window function operates independently. It’s similar to how GROUP BY works with aggregate functions, but instead of aggregating the rows into a single output for each group, PARTITION BY maintains the individual rows and applies the window function to each partition separately.
Question: What types of window functions are there in SQL, and can you describe their use cases?
Answer: There are several types of window functions, including:
- Ranking functions (e.g., ROW_NUMBER(), RANK(), DENSE_RANK()), which are used to assign a rank to each row within a partition of a result set.
- Aggregate functions (e.g., SUM(), AVG(), COUNT()), which, when used with OVER(), allow you to perform aggregate calculations on sets of rows without collapsing them into a single value.
- Analytic functions (e.g., LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE()), which are used for data analysis and comparison across rows within a partition.
Question: How does the ROWS BETWEEN clause enhance the functionality of window functions?
Answer: The ROWS BETWEEN clause specifies the range of rows around the current row within which the window function operates. This clause enhances the window function by allowing for precise control over which rows are included in the calculation for each row, enabling scenarios like moving averages or cumulative sums that are restricted to a specific range of rows before or after the current row.
Question: What are the different types of SQL joins and how do they differ?
Answer: The main types of SQL joins include:
- INNER JOIN: Returns rows when there is at least one match in both tables.
- LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table, and the matched rows from the right table, with NULLs where there are no matches.
- RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table, and the matched rows from the left table, with NULLs where there are no matches.
- FULL JOIN (or FULL OUTER JOIN): Combines LEFT JOIN and RIGHT JOIN, returning rows when there is a match in one of the tables.
- CROSS JOIN: Returns a Cartesian product of the two tables, matching every row of the first table with every row of the second table.
Question: In what scenarios would you use a LEFT JOIN over an INNER JOIN?
Answer: A LEFT JOIN is used over an INNER JOIN when you want to include all rows from the left table regardless of whether there is a matching row in the right table. This is particularly useful when you want to ensure that every row from the left table appears in the result set, even if there’s no corresponding row in the right table, which can help identify unmatched records or when you want to include default or placeholder values for missing data.
Question: How do window functions and JOIN operations interact in SQL queries?
Answer: Window functions and JOIN operations can be used together in SQL queries to perform complex data analysis and manipulation. For example, you might join multiple tables to consolidate related data into a single result set and then apply window functions to this result set to perform calculations like rankings, running totals, or moving averages. This combination allows for powerful and flexible data analysis within a single query operation.
Statistics Interview Questions
Question: What is the Central Limit Theorem and why is it important in statistics?
Answer: The Central Limit Theorem (CLT) states that the distribution of the sample means approaches a normal distribution as the sample size gets larger, regardless of the shape of the population distribution, provided the samples are independent and identically distributed. This theorem is crucial because it justifies the use of the normal distribution in many statistical procedures and confidence intervals, even when the population distribution is unknown, as long as the sample size is sufficiently large.
Question: Explain the difference between Type I and Type II errors.
Answer: A Type I error occurs when a true null hypothesis is incorrectly rejected. It’s also known as a “false positive” finding or conclusion. A Type II error happens when a false null hypothesis fails to be rejected, also known as a “false negative.” In simpler terms, a Type I error is mistakenly concluding something is true when it’s not, while a Type II error involves missing a truth.
Question: How would you explain p-value to a non-technical person?
Answer: A p-value is a measure that helps us understand the strength of our evidence against a null hypothesis. Imagine we’re trying to prove that a coin is biased. The p-value tells us how surprising our findings are, assuming the coin is fair. A very small p-value indicates that, if the coin were indeed fair, what we observed would be very unlikely, thus suggesting the coin may not be fair after all. It’s like saying, “If there were no effect or difference, the chances of seeing what we saw (or something more extreme) are very small.”
Question: What is the difference between correlation and causation?
Answer: Correlation refers to a statistical relationship or association between two variables, where changes in one variable are associated with changes in another. However, correlation does not imply that one variable causes the change in the other. Causation, on the other hand, indicates that a change in one variable is responsible for the change in another. In short, correlation can suggest a relationship, but only causation can prove one variable directly affects another.
Question: Can you explain what a confidence interval is and how it’s used?
Answer: A confidence interval is a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. For example, if we calculate a 95% confidence interval for the average amount of money spent by tourists in a city, we are saying we are 95% confident that the true average spending lies within this interval. Confidence intervals give us a range of plausible values for the parameter, reflecting the uncertainty of estimating population parameters from sample data.
Technical Interview Questions
- SQL (group by + having, pivot tables, types of joins)
- Stats (t-tests, z-tests, stats/math used in AB tests).
- Difference between bagging and boosting?
- How did you handle the feature selection process?
- What is Bias variance trade-off?
- Explain the meaning of overfitting to non-technical people.
Behavioral Interview Questions
Que: Why choose Expedia?
Que: How would you manage priorities?
Que: How do you measure if your test group is performing better than the control group?
Que: What’s the difficulty you meet in your life? How did you overcome the difficulty?
Que: Can you tell me about a time when you had to deal with a difficult customer or client? How did you handle it?
Que: Have you ever made a mistake at work? How did you handle it?
Que: Tell me about a time when you had to manage multiple projects simultaneously. How did you prioritize?
Que: Give an example of a goal you reached and tell me how you achieved it.
Conclusion
Interviewing at Expedia Group is an opportunity to showcase your analytical skills, creativity, and ability to derive meaningful insights from data. It’s important to prepare by understanding the types of questions that might be asked and practicing your responses. However, remember that interviews are a two-way street; they are also your chance to learn about the company and whether it aligns with your career aspirations. Good luck, and may your data always guide you to insightful conclusions!
