100+ Data Science Intern Interview Questions and Answers

The task is to rotate a square matrix by 90 degrees in an anti-clockwise direction using constant extra space.

• Iterate through each layer of the matrix from outer to inner

• For each layer, perform a four-way swap of elements

• Continue this process until all layers have been rotated

Get second highest element from an array of strings with O(N) time complexity and O(1) space complexity.

• Initialize two variables to store the highest and second highest elements.

• Traverse the array and update the variables accordingly.

• Return the second highest element.

• Handle edge cases like empty array or array with only one element.

Gradient descent is an optimization algorithm used to minimize the cost function of a machine learning model.

• Gradient descent is used to update the parameters of a model to minimize the cost function.

• It follows the direction of steepest descent, which is the negative gradient of the cost function.

• The learning rate determines the step size of the algorithm.

• The formula for gradient descent is: theta = theta - alpha * (1/m) * sum((hypothesis - y) * x)

• The cost function should be ...read more

I would like to add a feature that allows users to schedule rides in advance.

• Users can schedule rides for important events or appointments

• Option to choose specific driver or vehicle for scheduled rides

• Notifications for upcoming scheduled rides

• Ability to edit or cancel scheduled rides

A chessboard has 64 small squares, but also contains larger squares formed by combining them.

• A standard chessboard is 8x8, totaling 64 small squares.

• Larger squares can be formed: 1x1, 2x2, ..., up to 8x8.

• Count of squares of size kxk is (8-k+1)^2.

• Total squares = 1^2 + 2^2 + 3^2 + ... + 8^2 = 204.

Coffiecent of x^7 in a given equation

• Use the binomial theorem to expand the equation

• Identify the term with x^7

• The coefficient of x^7 is the coefficient of that term

I completed the Data Science course at XYZ University.

• Completed Data Science course at XYZ University

• Received hands-on training in machine learning algorithms

• Worked on real-world projects during the course

Choosing the optimal K value in K-means clustering is crucial for accurate results.

• Elbow method: Plotting the sum of squared distances vs. K and selecting the K value where the curve bends like an elbow.

• Silhouette method: Calculating the average silhouette score for different K values and choosing the one with the highest score.

• Gap statistic method: Comparing the within-cluster dispersion to a reference null distribution to find the optimal K value.

• Cross-validation: Splitting...read more

Random forest is an ensemble learning method that constructs a multitude of decision trees and outputs the mode of the classes. Gini impurity is a measure of impurity or randomness used in decision trees.

• Random forest is a collection of decision trees that are trained on different subsets of the data.

• Each decision tree in the forest is trained on a random subset of the features.

• The final prediction is made by taking the mode of the predictions of all the trees.

• Gini impurity i...read more

I used the Random Forest algorithm in my project.

• Random Forest is an ensemble learning method that combines multiple decision trees to make predictions.

• It is used for both classification and regression tasks.

• Random Forest reduces overfitting and provides feature importance.

• Example: I used Random Forest to predict customer churn in a telecom company.

Call by value passes a copy of the value while call by reference passes the address of the value.

• Call by value passes a copy of the value while call by reference passes the address of the value.

• Call by value does not modify the original value while call by reference can modify the original value.

• Call by value is used for simple data types while call by reference is used for complex data types.

To extract insights from unstructured data, I would employ exploratory analysis, NLP, and clustering techniques.

• 1. Data Exploration: Begin by understanding the data's structure and content. For example, if it's text data, look for common themes or topics.

• 2. Preprocessing: Clean the data by removing noise, such as irrelevant information or formatting issues. For instance, in text data, remove stop words and punctuation.

• 3. Natural Language Processing (NLP): Use NLP techniques t...read more

Joining tables combines related data for analysis, enhancing insights and decision-making.

• A join operation merges rows from two or more tables based on a related column.

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.

• Example: INNER JOIN returns only matching rows from both tables.

• LEFT JOIN returns all rows from the left table and matched rows from the right table.

• Example: If Table A has 3 rows and Table B has 2 matching rows, INNER JOIN will retu...read more

DDL is used to define the structure of database objects, while DML is used to manipulate data within those objects.

• DDL is used to create, modify, and delete database objects such as tables, indexes, and views.

• DML is used to insert, update, retrieve, and delete data within those database objects.

• Examples of DDL statements include CREATE TABLE, ALTER INDEX, and DROP VIEW.

• Examples of DML statements include INSERT INTO, UPDATE SET, and DELETE FROM.

Choosing the right performance metric depends on the specific goals and context of the model's application.

• Consider the business objective: For example, in fraud detection, prioritize recall to catch as many fraudulent cases as possible.

• Evaluate the cost of false positives vs. false negatives: In medical diagnosis, a false negative can be more harmful than a false positive.

• Use domain knowledge: In a recommendation system, precision might be more important to ensure users rece...read more

Find students who scored more than avg marks in both 11th and 12th grades.

• Calculate the average marks for each student in 11th and 12th grades.

• Compare each student's marks with the respective average marks to find those who scored higher in both grades.

LDA stands for Latent Dirichlet Allocation, a topic modeling technique used in natural language processing.

• LDA is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

• It is commonly used in text mining to extract topics from a collection of documents.

• LDA assumes that each document is a mixture of a small number of topics and that each word's presence is attributable to one of t...read more

Estimating daily tea cup consumption in Delhi involves population size, tea-drinking habits, and cultural factors.

• Delhi's population is approximately 20 million.

• Assuming 30% of the population drinks tea daily, that's 6 million tea drinkers.

• If each tea drinker consumes an average of 2 cups per day, that totals 12 million cups.

• Cultural factors: Tea is a popular beverage in India, often consumed at home and in street stalls.

Use a SQL query to find customers who have ordered all products from all categories.

• Join the Customers, Orders, and Products tables

• Group by customer and count the distinct products ordered

• Filter for customers who have ordered the total number of products available in each category

The average number of tea people drink in a day in Delhi varies depending on individual preferences and habits.

• The average number of tea consumed can range from 1-5 cups per day.

• Factors such as age, gender, occupation, and cultural background can influence tea consumption.

• Some people may drink more tea in the morning for a caffeine boost, while others may prefer tea throughout the day for relaxation.

• Tea consumption may also vary based on the season, with more tea being consum...read more

SQL is a programming language used for managing and manipulating relational databases. A database is a structured collection of data.

• SQL is used to retrieve, insert, update, and delete data from a database.

• A database is a software system that stores and organizes data in a structured manner.

• SQL allows users to define the structure of a database, create tables, and establish relationships between tables.

• Examples of databases include MySQL, Oracle, and SQL Server.

The SQL query for calculating a moving average involves using window functions.

• Use the OVER clause with the ORDER BY clause to define the window frame for the moving average calculation.

• Use the AVG() function to calculate the average within the window frame.

• Example: SELECT value, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table_name;

Joins in SQL are used to combine rows from two or more tables based on a related column between them.

• Joins are used to retrieve data from multiple tables in a single query.

• Common types of joins include inner join, left join, right join, and full outer join.

• Joins are performed using the JOIN keyword and specifying the columns to join on.

• Joins can be used to combine tables based on matching values or non-matching values.

• Joins help in creating relationships between tables and fe...read more

XGBoost is preferred over Random Forest for low bias models due to its ability to reduce bias further.

• XGBoost is a more complex algorithm compared to Random Forest, allowing it to reduce bias further in low bias models.

• XGBoost uses gradient boosting which helps in reducing bias by optimizing the loss function iteratively.

• Random Forest may not be able to further reduce bias in low bias models as effectively as XGBoost.

• In scenarios where the model already has low bias, XGBoost'...read more

Overfitting is when a model is too complex and fits the training data too closely, leading to poor performance on new data.

• Regularization techniques like L1 and L2 can be used to prevent overfitting

• Cross-validation can be used to evaluate model performance on new data

• Reducing the complexity of the model can also help prevent overfitting

• Handling missing values can be done by imputing them with mean, median or mode values

• Alternatively, missing values can be dropped if they are ...read more

Faced a challenge optimizing a complex SQL query that was slowing down data retrieval for a project.

• Identified the issue during testing when query execution time exceeded acceptable limits.

• Analyzed the query plan and discovered missing indexes on key columns.

• Implemented indexing strategies, which reduced execution time from 30 seconds to under 2 seconds.

• Collaborated with team members to ensure the changes did not affect other parts of the application.

Deep learning excels in handling complex data patterns, automating feature extraction, and improving accuracy over traditional methods.

• Deep learning can automatically extract features from raw data, reducing the need for manual feature engineering. For example, in image recognition, convolutional neural networks (CNNs) can identify edges, shapes, and objects without explicit programming.

• It is particularly effective for large datasets, where traditional machine learning may st...read more