100+ Data Science Intern Interview Questions and Answers for Freshers

Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.

• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given input belongs to a particular category.

• The model calculates the odds of the event happening.

• It uses a logistic function to map the input values to the output probability.

• Example: Predicting whether an emai...read more

Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.

• Supervised learning requires a target variable to predict, while unsupervised learning does not.

• In supervised learning, the model learns from the labeled training data and makes predictions on new data. In unsupervised learning, the model finds patterns and relationships in the data without guidance.

• Examples of supervised learning include classification and regression tas...read more

I primarily use Python for data science projects.

• Python is widely used in the data science community for its simplicity and versatility.

• It has a large number of libraries and frameworks specifically designed for data analysis and machine learning, such as Pandas, NumPy, and Scikit-learn.

• Python's syntax is easy to learn and understand, making it a popular choice for beginners and experienced programmers alike.

Large Language Models are advanced AI models that can generate human-like text based on input data.

• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.

Python is a versatile programming language used for data analysis, ML involves creating algorithms that improve automatically through experience, AI is the simulation of human intelligence by machines.

• Python is a popular programming language known for its simplicity and readability.

• Machine Learning (ML) involves creating algorithms that can learn from and make predictions or decisions based on data.

• Artificial Intelligence (AI) is the simulation of human intelligence processes...read more

Fax queries in Power BI are used to send queries to a data source via fax.

• Fax queries allow users to send queries to a data source using fax technology.

• This feature is useful for organizations that still rely on fax communication for data retrieval.

• Fax queries can be set up in Power BI to automate the process of sending and receiving data via fax.

Regularization in machine learning is a technique used to prevent overfitting by adding a penalty term to the model's loss function.

• Regularization helps in reducing the complexity of the model by penalizing large coefficients.

• Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization.

• L1 regularization adds the absolute value of the coefficients to the loss function, promoting sparsity.

• L2 regularization adds the squared value of the coefficients to the los...read more

Random forest is an ensemble learning method used for classification and regression tasks, consisting of multiple decision trees.

• Random forest is made up of multiple decision trees, where each tree is built using a subset of the training data and a random subset of features.

• During prediction, each tree in the forest independently predicts the output, and the final output is determined by a majority vote (classification) or averaging (regression) of all the trees' predictions....read more

Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.

• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the highest probability.

Correlation is a statistical measure that describes the extent to which two variables change together.

• Correlation ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no correlation.

• Correlation does not imply causation, meaning just because two variables are correlated does not mean that one causes the other.

• Examples of correlation include the relationship between temperature and ice cream sales,...read more

Logistic regression is used for binary classification while linear regression is used for regression tasks.

• Logistic regression predicts the probability of a binary outcome (0 or 1) based on one or more independent variables.

• Linear regression predicts a continuous outcome based on one or more independent variables.

• Logistic regression uses a sigmoid function to map predicted values between 0 and 1, while linear regression uses a linear function.

• Logistic regression is commonly u...read more

Random partition involves splitting data randomly, while ordering partition involves splitting data based on a specific order.

• Random partition randomly divides data into subsets without any specific order.

• Ordering partition divides data into subsets based on a specific order, such as time or alphabetical order.

• Random partition is useful for creating training and testing sets for machine learning models.

• Ordering partition is helpful for time series data analysis or when data n...read more

Generate all possible subsets of a given list in Python.

• Use itertools.combinations to generate all possible combinations of the list elements.

• Convert the combinations to lists and store them in a new list to get all subsets.

Naive Bayes assumes independence between features, hence 'naive'.

• Naive Bayes assumes all features are independent of each other, which is often not true in real-world data.

• Despite its simplifying assumption, Naive Bayes is still widely used in text classification and spam filtering.

• The 'naive' assumption allows for fast and efficient classification, especially with large datasets.

GANs are Generative Adversarial Networks, a type of deep learning model consisting of two neural networks - a generator and a discriminator.

• GANs are used to generate new data samples that resemble a given dataset.

• The generator network creates fake data samples, while the discriminator network tries to distinguish between real and fake samples.

• The two networks are trained simultaneously in a competitive manner, improving each other's performance.

• GANs have applications in image...read more

Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.

• Python basics cover syntax, variables, data types, and control structures.

• NumPy is a library for numerical computing, providing powerful array operations.

• Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.

• Matplotlib is a library for data visualization, allowing creation of various p...read more

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

• It involves formulating a hypothesis about a population parameter, collecting data, and using statistical tests to determine if the data supports or rejects the hypothesis.

• There are two types of hypotheses: null hypothesis (H0) and alternative hypothesis (H1).

• Common statistical tests for hypothesis testing include t-tests, ANOVA, chi-square tests, and regression analysis...read more

Population refers to the entire group of individuals or items that we are interested in studying, while a sample is a subset of the population.

• Population is the larger group that we want to draw conclusions about.

• Sample is a smaller group selected from the population to represent it.

• Population parameters are characteristics of the entire group, while sample statistics are characteristics of the sample.

• Example: Population could be all students in a school, while a sample could...read more

Cost function is a mathematical function that measures the error between predicted values and actual values in a machine learning model.

• Cost function helps in optimizing the parameters of a model to minimize the error.

• Common cost functions include Mean Squared Error (MSE) and Cross Entropy Loss.

• It is used in training machine learning models through techniques like gradient descent.

• The goal is to find the parameters that minimize the cost function.

Gini coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents.

• Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.

• A Gini coefficient of 0.4 is considered moderate inequality, while 0.6 or higher is considered high inequality.

• It is commonly used in economics to measure income inequality within a population.

• The formula for calculating Gini coefficie...read more

RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.

• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that are on schedule or completed successfully.

• For example, a pro...read more

OOP in Python is a programming paradigm where objects are created that contain data and methods to manipulate that data.

• Classes are used to create objects in Python

• Objects have attributes (data) and methods (functions)

• Inheritance allows classes to inherit attributes and methods from other classes

• Encapsulation allows data to be hidden and only accessed through methods

• Polymorphism allows objects to be treated as instances of their parent class

Convolution 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 point.

• It is commonly used in signal processing, image processing, and neural networks.

• Example: Convolutional Neural Networks (CNNs) use convolution layers to extract features from input data.

CNN model uses convolutional layers to extract features from images and classify them.

• Convolutional layers extract features from images by sliding a filter over the image and performing element-wise multiplication and summation

• Pooling layers reduce the spatial dimensions of the feature maps while retaining important information

• Fully connected layers use the extracted features to classify the image into different categories

• Examples include image classification, object detectio...read more

LSTM stands for Long Short-Term Memory, a type of recurrent neural network architecture used for sequence prediction and time series forecasting.

• LSTM networks are designed to overcome the vanishing gradient problem in traditional RNNs.

• They have the ability to remember long-term dependencies in data sequences.

• LSTMs have three gates: input gate, forget gate, and output gate, which control the flow of information.

• They are widely used in natural language processing, speech recogn...read more

Neural networks are a type of machine learning algorithm inspired by the human brain's neural structure.

• Neural networks consist of layers of interconnected nodes (neurons) that process input data and pass it through activation functions.

• They use weights to adjust the strength of connections between neurons during training.

• Neural networks are capable of learning complex patterns and relationships in data, making them suitable for tasks like image recognition and natural langua...read more

Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables. Logistic regression is used to model the probability of a binary outcome.

• Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.

• In linear regression, the relationship between the independent and dependent variables is assumed to be linear, while in logistic regression, th...read more

Yes, I am available for 3 months.

• Yes, I am available for the entire duration of 3 months.

• I have no prior commitments that would prevent me from completing the internship.

• I am eager to dedicate my time and effort to the internship.

Boosting algorithms are ensemble learning techniques that combine multiple weak learners to create a strong learner.

• Boosting algorithms train models sequentially, with each model correcting errors made by the previous one.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• AdaBoost adjusts the weights of incorrectly classified instances, Gradient Boosting fits new models to the residuals of the previous model, and XGBoost uses a more regularized m...read more

Feature engineering is crucial in data science as it involves selecting, transforming, and creating new features to improve model performance.

• Feature engineering helps in improving model accuracy by providing relevant and meaningful input variables.

• It involves techniques like one-hot encoding, scaling, normalization, and creating interaction terms.

• Feature engineering can help in reducing overfitting and improving model interpretability.

• Examples include creating new features f...read more

Find the 2nd highest number in an array of strings.

• Convert the array of strings to an array of integers for comparison.

• Sort the array in descending order to easily find the 2nd highest number.

• Return the element at index 1 as the 2nd highest number.

KNN is a supervised learning algorithm used for classification and regression, while K Means is an unsupervised clustering algorithm.

• KNN stands for K-Nearest Neighbors and assigns a class label based on majority voting of its k-nearest neighbors.

• K Means is a clustering algorithm that partitions data into k clusters based on similarity.

• KNN requires labeled data for training, while K Means does not need labeled data.

• KNN is a lazy learner as it does not learn a discriminative fu...read more

Bias in ML refers to the systematic error in a model's predictions, leading to inaccurate results.

• Bias is the algorithm's tendency to consistently learn the wrong thing by not taking all factors into account.

• It can result from the data used to train the model being unrepresentative or skewed.

• Bias can lead to unfair or discriminatory outcomes, especially in sensitive areas like hiring or lending decisions.

• Examples include gender bias in resume screening algorithms or racial bi...read more

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.

• Linear regression aims to find the best-fitting straight line that describes the relationship between the variables.

• It is commonly used for predicting future values based on past data.

• The equation for a simple linear regression model is y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b is ...read more

Supervised ML is a type of machine learning where the model is trained on labeled data to make predictions or decisions.

• Supervised ML requires a dataset with input variables and corresponding output labels.

• The model learns from the labeled data to make predictions on new, unseen data.

• Examples include classification tasks like spam detection or regression tasks like predicting house prices.

Python has several built-in data types including numeric, sequence, mapping, and set types.

• Numeric types include integers, floats, and complex numbers.

• Sequence types include lists, tuples, and strings.

• Mapping types include dictionaries.

• Set types include sets and frozensets.

• Each data type has its own set of methods and operations.

• Examples: int(5), float(3.14), complex(2+3j), list([1,2,3]), tuple((1,2,3)), str('hello'), dict({'key': 'value'}), set({1,2,3}), frozenset({4,5,6})

Batch normalization is a technique used to improve the training of deep neural networks.

• Batch normalization normalizes the inputs of each layer by subtracting the batch mean and dividing by the batch standard deviation.

• It helps in reducing the internal covariate shift and improves the convergence of the network.

• It also acts as a regularizer and reduces the need for dropout.

• It is widely used in deep learning models like CNNs and LSTMs.

• Example: In a CNN, batch normalization can...read more

I like data because it allows me to uncover insights, make informed decisions, and solve complex problems.

• Data helps me understand trends and patterns in various industries.

• I enjoy using data to drive business strategies and improve processes.

• Analyzing data allows me to make evidence-based decisions and solve real-world problems.

• Data visualization helps me communicate findings effectively to stakeholders.

Entropy is a measure of disorder or randomness in a system.

• Entropy is used in information theory to quantify the amount of uncertainty involved in predicting the value of a random variable.

• It is often used in machine learning to measure the impurity or disorder in a dataset.

• In thermodynamics, entropy is a measure of the amount of energy in a physical system that is not available to do work.

Natural language processing is a branch of artificial intelligence that focuses on the interaction between computers and humans using natural language.

• NLP involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.

• It uses algorithms and models to analyze and understand human language, enabling computers to process, interpret, and generate text.

• Examples of NLP applications include chatbots, virtual assistants like Siri and...read more

Reinforcement learning is a type of machine learning where an agent learns to make decisions by receiving feedback from its environment.

• In reinforcement learning, an agent interacts with an environment by taking actions and receiving rewards or penalties.

• The goal is for the agent to learn the optimal strategy to maximize cumulative rewards over time.

• Examples include training a computer program to play games like chess or Go, or optimizing a robot's movements in a physical env...read more

Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.

• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random forest helps to improve accuracy and prevent overfitting....read more

Yes, I have published a research paper on the topic of machine learning algorithms for predictive analytics.

• Published research paper on machine learning algorithms

• Focused on predictive analytics

• Presented findings at a data science conference

SQL is a relational database management system, while NoSQL is a non-relational database management system.

• SQL databases are table-based and have a predefined schema, while NoSQL databases are document-based, key-value pairs, graph databases, or wide-column stores.

• SQL databases are good for complex queries and transactions, while NoSQL databases are better for hierarchical data storage and real-time web applications.

• Examples of SQL databases include MySQL, Oracle, and Postgre...read more

DS stands for Data Science, ML stands for Machine Learning, and AI stands for Artificial Intelligence.

• Data Science (DS) involves extracting insights and knowledge from data.

• Machine Learning (ML) is a subset of AI that allows systems to learn and improve from experience.

• Artificial Intelligence (AI) is the simulation of human intelligence processes by machines.

• Example: Using data science to analyze customer behavior, implementing machine learning algorithms for predictive analy...read more

Regression in ML is a supervised learning technique used to predict continuous values based on input features.

• Regression models establish a relationship between dependent and independent variables.

• Common regression algorithms include linear regression, polynomial regression, and logistic regression.

• Evaluation metrics for regression models include Mean Squared Error (MSE) and R-squared.

• Example: Predicting house prices based on features like size, location, and number of bedroo...read more

BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.

• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For example, in sentiment analysis tasks, BERT has shown higher a...read more