LinkedIn, a hub for professional networking, attracts top talent in the fields of data science and analytics. Job seekers aiming to land roles at LinkedIn in these domains often face rigorous interviews that assess their technical skills, problem-solving abilities, and knowledge of key concepts. To help you prepare effectively, we’ve compiled a list of common interview questions and answers in data science and analytics that you might encounter at LinkedIn.
Table of Contents
Technical Interview Questions
Question: Explain A/B testing.
Answer: A/B testing, also known as split testing, is a method used to compare two versions of a web page, app feature, or any other product component to determine which one performs better. Essentially, it’s an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation drives a more favorable outcome.
Question: Which is Ridge regression?
Answer: Ridge regression, also known as Tikhonov regularization, is a technique used to analyze multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. By adding a degree of bias to the regression estimates, ridge regression reduces the standard errors.
Question: Write window functions in SQL?
Answer: Window functions in SQL are used to perform calculations across a set of rows related to the current row in a way that is both powerful and flexible. Unlike regular aggregate functions, which group multiple rows into a single output value, window functions operate on a set of rows while still returning a single value for every input row. They are particularly useful for tasks involving rankings, running totals, moving averages, and more.
Question: What is the KNN algorithm in Python?
Answer: The K-Nearest Neighbors (KNN) algorithm in Python is a simple, yet powerful machine learning algorithm used for classification and regression. It works by finding the ‘k’ closest data points (neighbors) to a given point and predicting the label (in classification) or value (in regression) based on these neighbors. KNN is a type of instance-based or lazy learning where the function is only approximated locally, and all computation is deferred until classification.
Question: Explain the difference between the inner join and left join.
Answer:
- INNER JOIN: Returns rows when there is at least one match in both tables, showing only the intersecting records.
- LEFT JOIN: Returns all rows from the left table, and the matching rows from the right table, with NULL values for non-matching rows on the right side. Useful for including all records from the left table, whether or not they have matches in the right table.
Question: Explain CTE.
Answer: A Common Table Expression (CTE) in SQL is a temporary result set that allows for the definition of named, temporary tables within a query. It improves query readability by breaking down complex logic into smaller, more manageable parts. CTEs can be referenced within the same query to avoid code duplication and enhance code organization.
Question: What is Aggregation?
Answer: Aggregation in SQL refers to the process of applying functions such as SUM(), AVG(), COUNT(), MIN(), or MAX() to a set of values to obtain a single summary value. It involves combining multiple rows of data into a single result based on specified criteria. Aggregation functions are commonly used to calculate totals, averages, counts, or other summary statistics from data stored in a database table. For example, calculating the total sales amount, and average salary, or counting the number of orders placed.
Question: What is the optimization problem for an SVM?
Answer: The optimization problem for a Support Vector Machine (SVM) involves finding the hyperplane that best separates the data points into different classes while maximizing the margin between the hyperplane and the closest data points (support vectors). In other words, SVM aims to find the optimal hyperplane that not only separates the classes but also generalizes well to new, unseen data.
Question: Explain variance.
Answer: Variance is a statistical measure that quantifies the spread or dispersion of a set of data points around their mean or average. In simpler terms, it describes how much the data points deviate from the average value.
A high variance indicates that the data points are spread out widely from the mean, suggesting that the data set is diverse.
On the other hand, a low variance indicates that the data points are clustered closely around the mean, indicating a more uniform or less diverse data set.
Question: What is the Central Limit Theorem (CLT)?
Answer: The Central Limit Theorem 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.
Question: Explain the difference between Type I and Type II errors.
Answer: Type I error (false positive) occurs when we reject a true null hypothesis. Type II error (false negative) occurs when we fail to reject a false null hypothesis.
Question: What is the p-value in hypothesis testing?
Answer: The p-value is the probability of observing a test statistic at least as extreme as the one calculated from the sample data, assuming the null hypothesis is true. A lower p-value indicates stronger evidence against the null hypothesis.
Question: What is the difference between correlation and causation?
Answer: Correlation refers to a statistical relationship between two variables, while causation implies that one variable directly influences the other. Correlation does not imply causation.
Question: Explain the Normal Distribution.
Answer: The Normal Distribution, also known as the Gaussian Distribution, is a bell-shaped symmetrical distribution characterized by its mean and standard deviation. It is commonly used in statistics due to its properties such as the 68-95-99.7 rule (68% within one standard deviation, 95% within two, and 99.7% within three).
Question: What is the Binomial Distribution?
Answer: The Binomial Distribution models the number of successes in a fixed number of independent Bernoulli trials, where each trial has the same probability of success. It is characterized by two parameters: the number of trials (n) and the probability of success (p).
Question: Explain the Poisson Distribution.
Answer: The Poisson Distribution models the number of events occurring in a fixed interval of time or space, given a known average rate of occurrence. It is characterized by a single parameter, the average rate (λ).
Question: What is the Exponential Distribution?
Answer: The Exponential Distribution models the time between events in a Poisson process, where events occur continuously and independently at a constant average rate (λ). It is characterized by the rate parameter (λ).
Python and SQL Interview Questions
Question: What is Pandas in Python and why is it used in data science?
Answer: Pandas is a Python library used for data manipulation and analysis. It provides powerful data structures like DataFrame and Series, making it easier to work with structured data for tasks such as cleaning, transforming, and analyzing data.
Question: Explain the use of NumPy in Python for data science.
Answer: NumPy is a fundamental library for numerical computing in Python. It provides support for arrays, matrices, and mathematical functions, making it essential for tasks like linear algebra, statistical operations, and handling multidimensional data.
Question: How do you handle missing values in a Pandas DataFrame?
Answer: Missing values in a Pandas DataFrame can be handled using methods like fillna() to replace missing values with a specific value, dropna() to drop rows or columns with missing values, or using interpolation methods like ffill or bfill to fill missing values with nearby values.
Question: Write a SQL query to calculate the total revenue from a Sales table.
Answer:
SELECT SUM(Revenue) AS TotalRevenue FROM Sales;
Question: Explain the difference between GROUP BY and HAVING clauses in SQL.
Answer: GROUP BY is used to group rows that have the same values into summary rows while HAVING is used to filter records returned by GROUP BY based on a specified condition.
Question: How would you join two tables in SQL?
Answer: Tables can be joined using various types of joins like INNER JOIN, LEFT JOIN, RIGHT JOIN, or FULL JOIN based on the desired relationship between the tables.
Question: Write an SQL query to find the top 5 customers with the highest total order amounts.
Answer:
SELECT CustomerID, SUM(OrderAmount) AS TotalOrderAmount
FROM Orders
GROUP BY CustomerID
ORDER BY TotalOrderAmount DESC LIMIT 5;
Technical Interview Topics
- CTE, aggregations, grouping, ranks
- Question on self joins
- DSA Array Binary search
- SQL queries
- Basic stats questions related to standard deviation, probability distributions
- SQL Manipulation Questions
- Data Science & ML
General Behavioral Question
Que: How many active members in LinkedIn right now?
Que: What is the business model at LinkedIn?
Que: What do you expect from the role
Que: What product metrics do you construct?
Que: How to tell if your experiment is successful?
Conclusion
By preparing for these data science and analytics interview questions, you’ll be equipped with the knowledge and confidence to tackle LinkedIn’s challenging interviews. Remember to not only understand the concepts but also practice applying them to real-world scenarios. Best of luck on your journey to a successful career at LinkedIn!
