20+ Data Scientist Intern Interview Questions and Answers for Freshers

No, compromising project quality is not an option even if the deadline is approaching.

• Quality should never be compromised as it reflects the professionalism and credibility of the work.

• Instead of compromising quality, it is better to communicate with the team and stakeholders to find alternative solutions.

• Prioritize tasks, optimize processes, and work efficiently to meet the deadline without sacrificing quality.

• Seek help or delegate tasks if necessary to ensure both quality a...read more

Implement a function to find the maximum product of two integers in an array.

• Iterate through the array and keep track of the two largest and two smallest integers.

• Calculate the products of the largest and smallest integers and return the maximum product.

Hypothesis testing is a statistical method used to make inferences about a population based on sample data.

• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• It helps determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

• Example: Testing whether a new drug is effective by comparing the recovery rates of a treatment group and a control group.

• Other examples include testing the impact of ad...read more

Identify even/odd numbers, loop through a list, and handle edge cases effectively.

• An even number is divisible by 2 (e.g., 2, 4, 6).

• An odd number is not divisible by 2 (e.g., 1, 3, 5).

• Use the modulus operator (%) to check: number % 2 == 0 for even.

• Loop through a list using a for loop: for number in list.

• Handle edge cases like empty lists or non-integer values.

• Example: For list [1, 2, 3, 4], output would be 'Odd: 1, 3' and 'Even: 2, 4'.

OOP is a programming paradigm based on objects, promoting code reusability and organization through key concepts like encapsulation and inheritance.

• Encapsulation: Bundling data and methods that operate on the data within one unit (class). Example: A class 'Car' with attributes like 'color' and methods like 'drive()'.

• Inheritance: Mechanism to create a new class from an existing class, inheriting its properties. Example: 'ElectricCar' inherits from 'Car'.

• Polymorphism: Ability t...read more

Different algorithms suit various types of raw data, impacting analysis and predictions.

• 1. Structured Data: Use algorithms like Linear Regression or Decision Trees. Example: Predicting house prices based on features.

• 2. Unstructured Data: Use NLP techniques or Convolutional Neural Networks (CNNs). Example: Image classification or sentiment analysis.

• 3. Time Series Data: Use ARIMA or LSTM models. Example: Stock price forecasting.

• 4. Categorical Data: Use algorithms like Random Fo...read more

Conditional probability measures the likelihood of an event given that another event has occurred.

• Conditional probability is denoted as P(A|B), meaning the probability of A given B.

• Example: If 30% of people have a cold and 10% of those with a cold have a cough, P(cough|cold) = 0.1.

• It is calculated using the formula: P(A|B) = P(A and B) / P(B).

• In a deck of cards, if you know a card is a heart, the probability it is a queen is P(Queen|Heart) = 1/13.

Utilize machine learning models to predict employee attrition and take proactive measures to reduce it.

• Collect relevant data such as employee demographics, performance metrics, satisfaction surveys, etc.

• Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.

• Split the data into training and testing sets to train the model and evaluate its performance.

• Choose appropriate machine learning algorithms such as logistic regressi...read more

Convolution operation is a mathematical operation that combines two functions to produce a third function.

• Convolution involves sliding one function over another and multiplying the overlapping values at each position.

• It is commonly used in image processing and signal processing to extract features.

• In deep learning, convolutional neural networks use convolution operations to learn spatial hierarchies of features.

PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving the most important information.

• PCA helps in identifying patterns in data by reducing the number of variables

• It finds the directions (principal components) along which the variance of the data is maximized

• PCA is commonly used in image processing, genetics, and finance

I have used libraries like NumPy, Pandas, Matplotlib, and Scikit-learn in Python for data analysis and machine learning tasks.

• NumPy: Used for numerical computing and array operations.

• Pandas: Used for data manipulation and analysis.

• Matplotlib: Used for data visualization.

• Scikit-learn: Used for machine learning algorithms and model building.

Central limit theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

• Central limit theorem is a fundamental concept in statistics.

• It states that the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution.

• It is important for making inferences about population parameters based on sample data.

• The theorem is used in hypothesi...read more

Pruning is a technique used in machine learning to reduce the size of decision trees by removing unnecessary branches.

• Pruning helps prevent overfitting by simplifying the model

• There are two types of pruning: pre-pruning and post-pruning

• Pre-pruning involves setting a limit on the depth of the tree or the number of leaf nodes

• Post-pruning involves removing branches that do not improve the overall accuracy of the tree

• Example: Removing a branch that only contains data points from ...read more

Use pandas library to read csv files in Python.

• Import pandas library: import pandas as pd

• Use pd.read_csv() function to read csv file

• Specify file path as argument in read_csv() function

• Assign the result to a variable to store the data

• Example: df = pd.read_csv('file.csv')

The assignment output results include data analysis findings and visualizations.

• Generated summary statistics for the dataset

• Created data visualizations using matplotlib or seaborn

• Performed hypothesis testing to draw conclusions

• Used machine learning algorithms for predictive modeling

Proficient in Python with experience in data analysis, machine learning, and automation.

• Used Python for data cleaning, manipulation, and visualization in projects

• Implemented machine learning algorithms using libraries like scikit-learn and TensorFlow

• Automated repetitive tasks using Python scripts and libraries like pandas and NumPy

Confusion matrix is a table used to evaluate the performance of a classification model.

• It is used to measure the accuracy of a classification model.

• It compares the predicted values with the actual values.

• It consists of four values: true positive, false positive, true negative, and false negative.

• It is commonly used in machine learning and data science.

• It helps in identifying the strengths and weaknesses of a model.

Machine learning algorithms are methods that enable computers to learn from data and make predictions or decisions.

• Supervised Learning: Algorithms like Linear Regression and Decision Trees use labeled data for training.

• Unsupervised Learning: Techniques such as K-Means Clustering and PCA find patterns in unlabeled data.

• Reinforcement Learning: Algorithms like Q-Learning learn optimal actions through trial and error in an environment.

• Deep Learning: Neural networks, especially Co...read more

Feature engineering is the process of selecting, modifying, or creating features to improve model performance.

• Identifying relevant features: Selecting variables that have predictive power, e.g., using age and BMI in health-related models.

• Creating new features: Combining existing features, like creating 'total income' from 'monthly salary' and 'annual bonus'.

• Handling missing values: Imputing missing data using mean, median, or mode to maintain dataset integrity.

• Encoding catego...read more

SQL queries allow users to retrieve and manipulate data from databases using structured commands.

• SELECT statement: Used to select data from a database. Example: SELECT * FROM employees;

• WHERE clause: Filters records based on specified conditions. Example: SELECT * FROM employees WHERE age > 30;

• JOIN operations: Combines rows from two or more tables based on a related column. Example: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id;

• GROUP BY clause: Group...read more

Random Forest is an ensemble learning algorithm used for classification and regression tasks.

• Random Forest builds multiple decision trees and combines their outputs to make a final prediction.

• It is a bagging algorithm that randomly selects a subset of features and data points for each tree.

• Random Forest reduces overfitting and improves accuracy compared to a single decision tree.

• It can handle missing values and outliers in the data.

• Example: Predicting whether a customer will ...read more

Data Science is a field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

• Data Science involves collecting, cleaning, analyzing, and interpreting large amounts of data to make informed decisions.

• It combines statistics, machine learning, data visualization, and domain expertise to solve complex problems.

• Examples include predicting customer behavior based on past purchase data, detecting fraud in financ...read more

The sum of two numbers is the result of adding them together.

• Add the two numbers together to get the sum

• The sum of 5 and 3 is 8 (5 + 3 = 8)

• The sum of -2 and 7 is 5 (-2 + 7 = 5)