When gearing up for an interview in data science and analytics at Agoda, a leading travel platform, you should be well-prepared to tackle a range of topics that blend technical proficiency with analytical acumen. Here, we delve into some common interview questions you might encounter, along with strategic answers that could help you stand out.
Table of Contents
Technical Interview Questions
Question: Difference between GBM and XGBOOST?
Answer:
- Speed and Performance: XGBoost is optimized for speed and performance. It is faster than GBM due to its capability to do parallel computing and effectively handle missing values internally.
- Regularization: XGBoost includes regularization (L1 and L2), which helps in reducing overfitting. In contrast, standard GBM lacks this feature, making XGBoost generally more robust against overfitting.
- Handling Large Datasets: XGBoost can handle larger datasets more efficiently. It supports out-of-core computing for very large datasets that don’t fit into memory, unlike GBM.
- Flexibility: XGBoost allows users to define custom optimization objectives and evaluation criteria, adding a layer of flexibility that GBM does not offer.
Question: Explain how CNN and RNN work.
Answer:
CNN (Convolutional Neural Networks):
- Functionality: CNNs are primarily used in image processing. They work by applying convolutional layers to the input, which filters and captures spatial hierarchies in data.
- Structure: A typical CNN architecture includes convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply a series of learned filters to extract features such as edges while pooling layers reduce the dimensionality of each feature map.
- Application: Ideal for tasks like image and video recognition, image classification, and medical image analysis.
RNNs (Recurrent Neural Networks):
- Functionality: RNNs are designed to handle sequential data. Each neuron or unit in an RNN can pass information forward from one step of the sequence to the next, effectively having a “memory” that captures information about what has been calculated so far.
- Structure: In its simplest form, an RNN has a loop within the network that passes the information from one step to the next. This loop allows information to be passed from one step of the sequence to another.
- Application: Commonly used for time series analysis, natural language processing, speech recognition, and anywhere sequential data is involved.
Question: What is the difference between xgboost and random forests?
Answer:
- Handling Overfitting: XGBoost employs regularization techniques like L1 and L2 regularization to prevent overfitting, while Random Forests rely on ensemble averaging and feature randomness.
- Gradient Boosting vs. Bagging: XGBoost uses a gradient boosting algorithm, which builds trees sequentially, optimizing the errors of previous trees. Random Forests use a bagging algorithm, where trees are built independently in parallel.
- Performance: XGBoost is often faster due to its optimized implementation, especially on large datasets. Random Forests can be slower due to the parallel nature of tree construction.
Question: What is the adjuster R-square?
Answer: The adjusted R-squared is a statistical measure that evaluates the goodness-of-fit of a regression model, adjusting for the number of predictors in the model. Unlike the regular R-squared, which tends to increase as more predictors are added to the model (even if they don’t improve the model’s performance), the adjusted R-squared penalizes the addition of unnecessary predictors. It provides a more accurate indication of how well the independent variables explain the variation in the dependent variable, accounting for model complexity.
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, given a sufficiently large sample size, the sampling distribution of the mean of any independent, random variable will be normally distributed, regardless of the underlying distribution. The importance of CLT lies in its ability to allow for making inferences about population parameters using sample statistics. This is particularly useful in the context of Agoda, where decisions need to be data-driven, and sampling provides a practical method for understanding large populations.
Question: Explain what a p-value is and how you use it to determine the significance of results.
Answer: A p-value is a measure used in hypothesis testing to help you determine the strength of your results. It represents the probability of observing a statistic (or one more extreme) assuming that the null hypothesis is true. In practical terms, a lower p-value means that there is stronger evidence against the null hypothesis. At Agoda, when conducting A/B tests to evaluate the effectiveness of different website layouts or promotional offers, the p-value helps determine whether the differences seen in conversion rates are statistically significant or just due to random chance.
Question: Can you describe a situation where you would use a t-test versus a chi-square test?
Answer: A t-test is used when you want to compare the means of two groups to see if they are significantly different from each other, typically with continuous data. For example, if Agoda wants to compare the average booking value between two different marketing campaigns, a t-test would be appropriate.
On the other hand, a chi-square test is used for categorical data to test the independence of two variables or the goodness of fit of a model. For example, Agoda might use a chi-square test to see if the booking frequency varies by category of accommodations (like budget vs. luxury) across different regions.
Question: Explain the difference between correlation and causation.
Answer: Correlation refers to a statistical relationship between two variables, indicating that they tend to change together, but it does not imply that one causes the other. Causation, on the other hand, indicates a relationship where one event directly affects another. For instance, at Agoda, seeing that higher user ratings correlate with higher rebooking rates does not mean that higher ratings cause rebookings. Other factors could drive both metrics. Understanding this difference is crucial for making informed business decisions and avoiding incorrect interpretations of data.
Question: How do you handle missing or corrupted data in a dataset?
Answer: Handling missing or corrupted data involves several steps:
- Identify: First, identify the missing or corrupted data by checking for inconsistencies, outliers, or null values.
- Assess: Evaluate the extent and nature of the missing data—whether it is random or systematic, and how it might impact the analysis.
- Impute or Remove: Depending on the assessment, you might choose to impute missing values using statistical methods like mean, median, mode imputation, or model-based methods, or you may decide to remove them entirely if they represent a small fraction of the dataset.
- Validate: Finally, validate the approach by checking how the imputation or removal affects the results of your analysis.
Data structures and algorithms Interview Questions
Question: What are the differences between arrays and linked lists?
Answer: Arrays and linked lists are both linear data structures, but they store data differently and have different strengths and weaknesses. Arrays store elements in contiguous memory locations, which allows for fast, O(1) time complexity access to any element using its index. However, arrays have a fixed size, which means adding elements might require creating a new array and copying elements over, an O(n) operation.
Linked lists, on the other hand, consist of nodes that are connected by pointers. They allow for dynamic memory utilization, and elements can be easily added or removed without reallocating the entire structure, providing an O(1) operation if you have direct access to the node concerned. However, accessing an element by its position in a linked list requires sequential traversal, which has a time complexity of O(n).
Question: Explain the concept of hashing.
Answer: Hashing is a technique used to uniquely identify a specific object from a group of similar objects. In the context of computing, it involves converting a large key into a small, practical integer value that represents the original string. This value is then used as an index in a hash table.
Question: What is a binary search tree (BST)?
Answer: A binary search tree (BST) is a binary tree in which each node has a comparable key (and an associated value) and satisfies the restriction that the key in any node is larger than the keys in all nodes in that node’s left subtree and smaller than the keys in all nodes in its right subtree. This property makes binary search trees efficient for operations like lookup, addition, and deletion, all of which have average case time complexities of O(log n), with n being the number of nodes in the tree. BSTs are particularly useful for implementing dynamic sets of ordered items and for efficient in-order traversal of items.
Question: Describe an algorithm to detect a cycle in a linked list.
Answer: A popular way to detect a cycle in a linked list is using Floyd’s Tortoise and Hare algorithm, which involves two pointers, slow and fast. Both pointers start at the head of the linked list. The slow pointer moves one step at a time, while the fast pointer moves two steps at a time. If there is a cycle in the list, the fast pointer will eventually meet the slow pointer within the cycle. If the fast pointer reaches the end of the list (null), there is no cycle.
Question: How would you implement a queue using two stacks?
Answer: To implement a queue using two stacks, you would use one stack for enqueuing items and the other stack for dequeuing items. When you enqueue an item, you simply push it onto the first stack. When you want to dequeue an item, you check if the second stack is empty. If it is, you pop all the items from the first stack and push them onto the second stack, which reverses the order of the elements. Then, you can simply pop the top element of the second stack. If the second stack is not empty, you pop the top item directly. This method ensures that all elements are in the correct order to simulate a queue’s FIFO behavior.
Python Interview Questions
Question: What are Python decorators and how do you use them?
Answer: Python decorators are a design pattern that allows you to modify the behavior of a function or class. They are used by placing them above a function’s definition with the @decorator_name syntax. Decorators are commonly used for logging, access control, or memoization.
Question: Explain the difference between list, set, and tuple.
Answer: A list is a mutable, ordered collection of elements. A set is an unordered collection of unique elements and is mutable. A tuple is an ordered, immutable collection of elements. Lists are good for sequential access, sets for fast membership testing, and tuples for storing fixed sequences of items.
Question: How does Python handle memory management?
Answer: Python uses an automatic memory management system that includes a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. Python also uses an in-built garbage collector, which recycles all the unused memory to make it available for heap space.
Question: What are Python’s built-in data types?
Answer: Python’s built-in data types include integers (int), floating-point numbers (float), complex numbers (complex), strings (str), lists (list), tuples (tuple), dictionaries (dict), sets (set), and booleans (bool), among others. These data types are essential for defining the nature of data used in an application.
Question: How can you manage packages and modules in Python?
Answer: Packages and modules in Python are managed through modules, which are simply Python files with a .py extension, and packages, which are a way of structuring Python’s module namespace by using “dotted module names”. The Python Package Index (PyPI) can be used to install packages globally, and tools like pip make it easy to install, upgrade, and remove packages.
Question: What is a lambda function in Python?
Answer: A lambda function in Python refers to a small anonymous function defined with the lambda keyword. Lambda functions can have any number of arguments but only one expression, which is evaluated and returned. They are often used for small, one-off functions that are not complex enough to warrant a full function definition.
SQL Interview Questions
Question: What is the difference between INNER JOIN, LEFT JOIN, and RIGHT JOIN in SQL?
Answer: INNER JOIN returns rows that have matching values in both tables. LEFT JOIN returns all rows from the left table and matched rows from the right table; if there is no match, the result is NULL on the right side. RIGHT JOIN works exactly opposite to LEFT JOIN, returning all rows from the right table and the matched rows from the left table, with NULLs in columns of the left table when there is no match.
Question: Explain the use of GROUP BY and HAVING clauses in SQL.
Answer: GROUP BY is used in conjunction with aggregate functions to group rows that have the same values in specified columns into summary rows. The HAVING clause is used to filter records that work on summarized GROUP BY results, allowing conditions to be applied that filter which group results appear in the final results.
Question: How do you implement pagination in SQL?
Answer: Pagination in SQL can be implemented using the OFFSET and FETCH clauses. For example, to retrieve the second set of 10 items from a table, you could use: SELECT * FROM table_name ORDER BY column_name OFFSET 10 ROWS FETCH NEXT 10 ROWS ONLY. This skips the first 10 records and fetches the next 10 records.
Question: What are indexes and why are they important?
Answer: Indexes are special lookup tables that the database search engine can use to speed up data retrieval. Simply put, an index is a pointer to data in a table. An index in a database is very similar to an index in the back of a book. They are particularly important for improving the speed of query operations on a database table at the cost of additional writes and storage space to maintain them.
Question: What is a subquery, and can you provide an example?
Answer: A subquery is a query within another query. The inner query is executed first, and its result is used in the execution of the outer query. For example, to find the names of employees who earn more than the average salary, one might use:
SELECT name FROM Employee
WHERE salary > (SELECT AVG(salary)
FROM Employee);
Question: What is normalization? Why is it important?
Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, less redundant tables and defining relationships between them. Normalization is important for minimizing duplicate data, simplifying queries, and ensuring data consistency within the database.
Conclusion
By preparing for these questions, candidates can demonstrate their technical capabilities and strategic thinking, key traits that Agoda looks for in prospective data scientists and analysts. This preparation will not only help you display your skill set but also your readiness to contribute to Agoda’s data-driven objectives.
