Preparing for a data science and analytics interview at IQVIA requires a solid understanding of core concepts, practical applications, and the ability to articulate your knowledge effectively. Here are some commonly asked questions and concise answers to help you ace your interview:
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Machine Learning Interview Questions
Question: Explain the difference between bagging and boosting.
Answer: Bagging involves training multiple models on different data subsets and averaging their predictions (e.g., Random Forest). Boosting trains models sequentially, focusing on previous errors, and combining their predictions (e.g., AdaBoost, XGBoost).
Question: What is a support vector machine (SVM)?
Answer: SVM is a supervised learning algorithm for classification and regression tasks, finding the hyperplane that best separates data into classes with maximum margin. It can handle linear and non-linear data using kernel functions.
Question: What is the bias-variance tradeoff?
Answer: The bias-variance tradeoff involves balancing underfitting (high bias) and overfitting (high variance). The goal is to create a model that generalizes well to new data.
Question: How does a convolutional neural network (CNN) work?
Answer: CNNs are used for image processing, consisting of convolutional layers that apply filters to capture spatial features, pooling layers to reduce dimensions, and fully connected layers for classification.
Question: Describe a machine learning project you have worked on.
Answer: I worked on predicting customer churn for a telecom company, using demographic data and service usage patterns. After preprocessing, I tested models like logistic regression and gradient boosting, selecting the best ROC-AUC model to identify high-risk customers for targeted retention.
Question: How do you handle imbalanced datasets in classification tasks?
Answer: Techniques include resampling (over-sampling minority/under-sampling majority class), synthetic data generation (SMOTE), algorithm modifications (adding class weights), and using appropriate evaluation metrics like precision-recall and ROC-AUC.
Python Interview Questions
Question: What are inheritance and polymorphism in Python?
Answer: Inheritance allows a class to inherit attributes and methods from another class, promoting code reuse. Polymorphism allows different classes to be treated as instances of the same class through shared methods.
Question: What is a module in Python?
Answer: A module is a file containing Python definitions and statements. It allows you to organize code into manageable, reusable pieces. Modules can be imported using the import statement.
Question: What is the purpose of the pandas library in Python?
Answer: pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames and Series, which facilitate data cleaning, transformation, and analysis tasks.
Question: What is a decorator in Python?
Answer: A decorator is a function that modifies the behavior of another function. Decorators are used to add functionality to existing functions in a reusable and clean way. They are defined with the @decorator_name syntax.
Question: What is a generator in Python?
Answer: A generator is a function that returns an iterator that yields one value at a time using the yield statement. Generators are memory-efficient as they generate values on the fly and do not store them in memory.
Question: How do you handle exceptions in Python?
Answer: Exceptions are handled using try, except, else, and finally blocks. Code that may raise an exception is placed in the try block, while the except block handles the exceptions. else runs if no exceptions occur, and finally executes regardless of the outcome.
SQL Interview Questions
Question: What is a transaction in SQL?
Answer: A transaction is a sequence of SQL statements that are executed as a single unit of work. Transactions ensure data integrity and are managed using BEGIN TRANSACTION, COMMIT, and ROLLBACK.
Question: What are the constraints in SQL?
Answer: Constraints are rules enforced on data columns to ensure data integrity. Examples include PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK.
Question: What are aggregate functions?
Answer: Aggregate functions perform calculations on multiple rows and return a single value. Examples include SUM(), COUNT(), AVG(), MIN(), and MAX().
Question: What is a JOIN in SQL?
Answer: A JOIN clause is used to combine rows from two or more tables based on a related column. Common types include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
Question: Explain the difference between INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns only the matching rows from both tables, while LEFT JOIN returns all rows from the left table and the matching rows from the right table, with NULLs for non-matching rows.
Tableau Interview Questions
Question: What is the purpose of a story in Tableau?
Answer: A story in Tableau is a sequence of sheets or dashboards that work together to convey information. Stories can be used to provide a guided narrative, illustrating a data-driven decision or analysis.
Question: What is the difference between filters and parameters in Tableau?
Answer: Filters are used to restrict the data displayed in visualizations based on specified criteria, while parameters are dynamic values that can be used to modify calculations, control inputs, or serve as inputs to filters.
Question: How do you use context filters in Tableau?
Answer: Context filters are applied before other filters and are used to set the context for subsequent filters. You can make a filter a context filter by right-clicking it and selecting Add to Context.
Question: What are LOD expressions in Tableau?
Answer: Level of Detail (LOD) expressions allow you to compute values at different levels of granularity, independent of the view’s level of detail. Examples include FIXED, INCLUDE, and EXCLUDE LOD expressions.
Question: How do you handle performance optimization in Tableau?
Answer: Performance optimization in Tableau can be handled by using extracts, minimizing the use of complex calculations, reducing the number of fields in views, optimizing data sources, and leveraging efficient data connections.
Question: How do you create a dual-axis chart in Tableau?
Answer: To create a dual-axis chart, drag two measures to the Rows or Columns shelf, then right-click on the second measure’s axis and select Dual Axis. You can synchronize the axes if needed.
DSA Interview Questions
Question: Explain the difference between BFS and DFS.
Answer: Breadth-First Search (BFS) explores all neighbor nodes at the present depth level before moving on to nodes at the next depth level. Depth-First Search (DFS) explores as far as possible along each branch before backtracking.
Question: What is time complexity?
Answer: Time complexity measures the amount of time an algorithm takes to run as a function of the input size. It describes the relationship between the input size and the number of operations performed by the algorithm.
Question: What is space complexity?
Answer: Space complexity measures the amount of memory space an algorithm requires as a function of the input size. It describes the relationship between the input size and the additional space required by the algorithm.
Question: Describe an efficient algorithm to find the nth Fibonacci number.
Answer: An efficient approach uses dynamic programming to store previously computed Fibonacci numbers and compute subsequent ones in O(n) time complexity.
Question: How do you detect a cycle in a linked list?
Answer: Use Floyd’s Cycle Detection Algorithm (Tortoise and Hare algorithm) where two pointers move at different speeds; if they meet, there’s a cycle.
Question: What is dynamic programming?
Answer: Dynamic programming is a technique to solve problems by breaking them down into overlapping subproblems and storing results to avoid redundant computations. It’s used in optimization problems.
Question: Explain the concept of recursion.
Answer: Recursion is a programming technique where a function calls itself directly or indirectly to solve a problem. It simplifies complex problems by dividing them into smaller identical problems.
Conclusion
Preparing for a data science and analytics interview at IQVIA involves demonstrating technical proficiency, problem-solving skills, and a clear understanding of how data can drive business value in the healthcare and life sciences sectors. By familiarizing yourself with these questions and answers, you can confidently navigate your interview and showcase your expertise. Good luck!
