Securing a data science or analytics role at Qualcomm, a prominent technology company at the forefront of innovation, requires a solid grasp of core concepts, problem-solving skills, and the ability to apply data-driven insights to business challenges. To help you excel in your interview, let’s dive into some common questions and answers that you might encounter during the process.
Table of Contents
Probability Interview Questions
Question: What is the difference between probability and statistics?
Answer:
- Probability deals with predicting the likelihood of future events based on a set of known information. It focuses on the theoretical aspects of random events and their outcomes.
- Statistics, on the other hand, involves collecting, analyzing, interpreting, presenting, and organizing data. It aims to make inferences or conclusions about a population based on a sample of data from that population.
Question: Explain the concept of conditional probability.
Answer: Conditional probability is the probability of an event occurring given that another event has already occurred. Mathematically, it is represented as: P(A∣B)=P(B)P(A∩B) where:
- P(A∣B) is the conditional probability of event A given event B,
- P(A∩B) is the probability of both events A and B occurring together, and
- P(B) is the probability of event B occurring.
Question: What is the difference between independent and dependent events?
Answer:
- Independent events: Two events are independent if the occurrence of one event does not affect the occurrence of the other. Mathematically, for independent events A and B, (A∩B)=P(A)⋅P(B).
- Dependent events: Two events are dependent if the occurrence of one event affects the occurrence of the other. Mathematically, for dependent events A and B, P(A∩B)=P(A)⋅P(B∣A).
Question: How would you calculate the expected value of a random variable?
Answer: The expected value E(X) of a discrete random variable X is calculated by multiplying each possible value of X by its probability, and then summing the products. E(X)=∑xi⋅P(X=xi)
Question: Explain the concept of the normal distribution.
Answer: The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution characterized by its bell-shaped curve. It is symmetric around the mean and describes many natural phenomena. The standard normal distribution has a mean of 0 and a standard deviation of 1.
Question: What is the law of large numbers?
Answer: The law of large numbers states that as the number of trials in a probability experiment increases, the experimental probability of an event will approach its theoretical probability. In simpler terms, the more times an experiment is repeated, the closer the average outcome will be to the expected value.
Question: What is the difference between a discrete and a continuous random variable?
Answer:
- Discrete random variable: Takes on a finite or countably infinite number of values. Examples include the number of students in a class or the number of heads in multiple coin flips.
- Continuous random variable: Takes on an infinite number of values within a range. Examples include height, weight, or time.
Question: How would you use probability in a real-world scenario, such as network traffic analysis?
Answer: In network traffic analysis, probability can be used to:
- Estimate the likelihood of network congestion during peak hours.
- Predict the probability of a security breach based on historical data.
- Calculate the probability of packet loss or delays during data transmission.
Statistics Interview Questions
Question: What is the difference between population and sample in statistics?
Answer:
- Population: Refers to the entire set of individuals, items, or data points under study. It includes all possible members that meet the criteria for inclusion in the study.
- Sample: A subset of the population that is selected to represent the larger group. Samples are used in statistical analyses when it is impractical or impossible to study the entire population.
Question: What is the Central Limit Theorem, and why is it important?
Answer: The Central Limit Theorem (CLT): States that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the shape of the population distribution. It is crucial because it allows us to make inferences about a population mean based on sample data, even when the population distribution is unknown or non-normal.
Question: Explain the concept of hypothesis testing.
Answer: Hypothesis Testing: A statistical method used to make decisions about a population parameter based on sample data. It involves:
- Formulating a null hypothesis (H0) and an alternative hypothesis (Ha).
- Choosing a significance level (α) to determine the probability of rejecting the null hypothesis when it is true.
- Conducting a test (such as a t-test, or z-test) and calculating a test statistic.
- Comparing the test statistic to a critical value or p-value to make a decision.
Question: What is the difference between Type I and Type II errors?
Answer:
- Type I Error: Occurs when the null hypothesis is rejected when it is true. It represents a false positive.
- Type II Error: Occurs when the null hypothesis is not rejected when it is false. It represents a false negative.
Question: How would you explain the concept of confidence intervals?
Answer: Confidence Interval: A range of values around a sample estimate (such as a mean or proportion) that is likely to contain the true population parameter with a specified level of confidence.
For example, a 95% confidence interval means that if the sampling process is repeated many times, the true population parameter will fall within the interval about 95% of the time.
Question: What are the assumptions of linear regression?
Answer:
- Linearity: The relationship between the dependent and independent variables is linear.
- Independence: The residuals (errors) are independent of each other.
- Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
- Normality: The residuals are normally distributed.
Data Structure and Algorithm Interview Questions
Question: What is the time complexity of various operations in a binary search tree (BST)?
Answer:
- Insertion: O(log n) in average case, O(n) in worst case (unbalanced tree).
- Deletion: O(log n) in average case, O(n) in worst case (unbalanced tree).
- Search (lookup): O(log n) in average case, O(n) in worst case (unbalanced tree).
Question: Explain the concept of dynamic programming.
Answer: Dynamic Programming: A method for solving complex problems by breaking them down into simpler subproblems. It stores the results of subproblems to avoid redundant computations.
It is often used to solve optimization problems, such as finding the shortest path, the longest common subsequence, or the minimum cost.
Question: What is the difference between an array and a linked list?
Answer:
- Array: A fixed-size data structure that stores elements of the same data type sequentially in memory. Accessing elements is fast, but insertion and deletion can be slow as it may require shifting elements.
- Linked List: A data structure where elements are stored in nodes, each containing a data field and a reference (or pointer) to the next node. Insertion and deletion are efficient, but accessing elements requires traversing the list.
Question: Explain the concept of Big O notation.
Answer: Big O notation: Describes the performance or complexity of an algorithm in terms of the input size. It represents the worst-case scenario of an algorithm’s time or space complexity.
For example, O(n) means linear time complexity, O(log n) means logarithmic time complexity, and O(1) means constant time complexity.
Question: What is the difference between BFS (Breadth-First Search) and DFS (Depth-First Search)?
Answer:
- BFS (Breadth-First Search): Explores all the neighbor nodes at the present depth before moving on to the nodes at the next depth level. Uses a queue data structure.
- DFS (Depth-First Search): Explores as far as possible along each branch before backtracking. Uses a stack (or recursion) data structure.
Question: What is the difference between a stack and a queue?
Answer:
- Stack: Follows Last In First Out (LIFO) order. Elements are added and removed from the top (or end) of the stack.
- Queue: Follows First In First Out (FIFO) order. Elements are added at the end (rear) and removed from the front (front) of the queue.
Conclusion
Remember, Qualcomm’s data science and analytics interviews assess not just your technical skills but also your problem-solving approach and ability to communicate insights effectively. Be prepared to discuss your past projects, explain your methodologies, and demonstrate a deep understanding of statistical and machine learning concepts.
