90+ Data Scientist Interview Questions and Answers for Freshers

Mission learning is used for data analysis and prediction with additional algorithms for AI.

• Mission learning is a subset of machine learning that focuses on predicting outcomes based on data analysis.

• It involves using algorithms to learn patterns and make predictions based on new data.

• Examples include image recognition, natural language processing, and recommendation systems.

Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.

• Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.

• Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.

• Linear Regression assumes a linear relationship between the variables...read more

The neighbourhood with the biggest median price difference between superhosts and non superhosts is X.

• Calculate the median price for superhosts and non superhosts in each neighbourhood

• Find the neighbourhood with the largest difference in median prices between superhosts and non superhosts

• Example: Neighbourhood X has a median price of $200 for superhosts and $150 for non superhosts, resulting in a $50 difference

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

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

• INNER JOIN returns rows when there is at least one match in both tables.

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

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

• FULL JOIN returns rows when there is a match in one of ...read more

Algorithms and law can be correlated through the use of algorithms in legal processes and decision-making.

• Algorithms can be used in legal research to analyze large amounts of data and identify patterns or trends.

• Predictive algorithms can be used in legal cases to assess the likelihood of success or failure.

• Algorithmic tools can help in legal document review and contract analysis.

• However, there are concerns about bias in algorithms used in law, as they can reflect and perpetua...read more

Find numbers that appear at least three times consecutively in any order.

• Use a window function to track consecutive numbers

• Filter the result to only include numbers that appear at least three times consecutively

• Return the result table in any order

Find the maximum number of vowels in any substring of length k in a given string.

• Iterate through the string with a sliding window of size k, counting vowels in each substring.

• Keep track of the maximum vowel count encountered.

• Return the maximum vowel count found.

No, confusion matrix is not used in Linear Regression.

• Confusion matrix is used to evaluate classification models.

• Linear Regression is a regression model, not a classification model.

• Evaluation metrics for Linear Regression include R-squared, Mean Squared Error, etc.

Data Scientist interview questions

• Introduced myself and my background

• Explained the difference between numpy and list

• Described Gradient Boosting and its applications

• Wrote a Python program to count letters in a string

• Explained my capstone project and its significance

• Discussed why I chose data science as a career

Find the longest common prefix string from a list of strings.

• Iterate through the characters of the first string and compare with corresponding characters of other strings

• Stop when a mismatch is found or when reaching the end of any string

• Return the prefix found so far

Optimizers in Deep Learning Models are algorithms used to minimize the loss function by adjusting the weights of the neural network.

• Optimizers help in updating the weights of the neural network during training to minimize the loss function.

• Popular optimizers include Adam, SGD, RMSprop, and Adagrad.

• Each optimizer has its own way of updating the weights based on gradients and learning rate.

• Choosing the right optimizer can significantly impact the training process and model perf...read more

Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.

• Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.

• Transformer model consists of an encoder and a decoder, both of which are...read more

Using R as a calculator to compute values for a Data Scientist interview question.

• Use R's console to input mathematical expressions and compute values.

• Make sure to follow the order of operations (PEMDAS) when entering expressions.

• Use functions like 'sqrt()' for square roots and 'exp()' for exponentiation.

• Remember to assign variables using the '<-' operator before using them in calculations.

Data science offers a new challenge and opportunity to apply analytical skills from my engineering background.

• Data science allows me to utilize my analytical skills in a new and challenging field.

• I can apply my knowledge of statistics and programming to extract insights from data.

• Data science offers opportunities to work on diverse projects and industries.

• My background in electrical engineering provides a strong foundation for understanding complex systems and data analysis.

Machine learning enables computers to learn from data and make predictions or decisions without being explicitly programmed.

• Machine learning can automate and optimize complex processes

• It can help identify patterns and insights in large datasets

• It can improve accuracy and efficiency in decision-making

• Examples include image recognition, natural language processing, and predictive analytics

• It can also be used for anomaly detection and fraud prevention

Dropout is a regularization technique to prevent overfitting by randomly setting some neuron outputs to zero during training. Batch Normalization is a technique to normalize the inputs of each layer to improve training speed and stability.

• Dropout randomly sets a fraction of neuron outputs to zero during training to prevent overfitting.

• Batch Normalization normalizes the inputs of each layer to improve training speed and stability.

• Dropout is commonly used in neural networks to ...read more

Isolation Forest is an anomaly detection algorithm that works by isolating outliers in a dataset.

• Isolation Forest is an unsupervised machine learning algorithm used for anomaly detection.

• It works by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

• The number of splits required to isolate an outlier is used as a measure of its abnormality.

• Evaluation metrics for Isolation Forest in layman's ter...read more

A happy number is a number which eventually reaches 1 when replaced by the sum of the square of each digit.

• Create a function to determine if a number is happy by repeatedly squaring the digits and summing them until the result is 1 or a cycle is detected.

• Use a set to keep track of seen numbers to detect cycles.

• Example: For number 19, the process would be 1^2 + 9^2 = 82, 8^2 + 2^2 = 68, 6^2 + 8^2 = 100, 1^2 + 0^2 + 0^2 = 1, so 19 is a happy number.

Transformer architecture is a type of neural network architecture commonly used in natural language processing tasks.

• Utilizes self-attention mechanism to weigh the importance of different words in a sentence

• Consists of encoder and decoder layers for tasks like machine translation

• Introduced by the paper 'Attention is All You Need' by Vaswani et al.

• Popular implementations include BERT, GPT, and TransformerXL

The ASCII value is a numerical representation of a character. It includes both capital and small alphabets.

• ASCII values range from 65 to 90 for capital letters A to Z.

• ASCII values range from 97 to 122 for small letters a to z.

• For example, the ASCII value of 'A' is 65 and the ASCII value of 'a' is 97.

My CGPA is low because I focused more on gaining practical experience through internships and projects.

• I prioritized gaining practical experience over theoretical knowledge

• I took up internships and projects to gain hands-on experience

• I believe practical experience is more valuable than just academic grades

Joints are connections between bones that allow movement and provide support to the body.

• Joints are found throughout the body, such as the knee, elbow, and shoulder.

• They are made up of bones, cartilage, ligaments, and synovial fluid.

• Joints enable various types of movements, including flexion, extension, rotation, and abduction.

• Different types of joints include hinge joints, ball-and-socket joints, and pivot joints.

• Joint problems can lead to conditions like arthritis and joint...read more

YOLO (You Only Look Once) is a real-time object detection system that processes images in a single pass, while SSD (Single Shot MultiBox Detector) is another object detection model that also aims for real-time processing but uses a different approach.

• YOLO processes images in a single pass, making it faster than SSD which requires multiple passes.

• SSD uses a fixed grid of boxes at different aspect ratios and scales to detect objects, while YOLO divides the image into a grid and...read more

My model solves the business problem by accurately predicting customer churn, allowing the company to proactively retain at-risk customers.

• The model uses historical customer data to identify patterns and factors leading to churn.

• It assigns a churn probability score to each customer, enabling targeted retention efforts.

• Regular model updates and monitoring ensure its effectiveness in reducing churn rates.

Methods for evaluating machine learning models include accuracy, precision, recall, F1 score, ROC curve, and confusion matrix.

• Accuracy: measures the proportion of correct predictions out of the total predictions made by the model.

• Precision: measures the proportion of true positive predictions out of all positive predictions made by the model.

• Recall: measures the proportion of true positive predictions out of all actual positive instances.

• F1 score: balances precision and recal...read more

I have used various software for data analysis including Python, R, SQL, Tableau, and Excel.

• Python - for data cleaning, manipulation, and modeling

• R - for statistical analysis and visualization

• SQL - for querying databases

• Tableau - for creating interactive visualizations

• Excel - for basic data analysis and visualization

Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy.

• Random Forest is a type of supervised learning algorithm used for classification and regression tasks.

• It creates multiple decision trees and combines their outputs to make a final prediction.

• Each decision tree is built using a random subset of features and data points to reduce overfitting.

• Random Forest is more accurate than a single decision tree an...read more

The kNN classifier is run on the iris data to make predictions based on nearest neighbors.

• kNN classifier is a type of supervised machine learning algorithm that can be used for classification tasks.

• The output will be the predicted class labels for the iris data based on the nearest neighbors.

• Interpreting the output involves understanding how the algorithm has classified the data points.

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

• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, which discourages the model from fitting the training data too closely.

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

• Regularization is important when dealing with high-dimension...read more

I'm not sure how joints relate to data science, but my hobby is playing guitar.

• Joints can refer to the connection between two bones in the body or the way two things are connected or joined together.

• Playing guitar is a hobby that helps me relax and unwind after a long day of working with data.

• While seemingly unrelated to data science, playing an instrument can actually improve cognitive function and creativity, which can be beneficial in the field.

Use string manipulation to efficiently extract numbers before the decimal point from a list of decimal numbers.

• Split each decimal number by the decimal point and extract the number before it

• Use regular expressions to match and extract numbers before the decimal point

• Iterate through the list and extract numbers using string manipulation functions

Lists and tuples are essential data structures in Python for storing and manipulating collections of items.

• Lists are mutable and allow for dynamic resizing, making them suitable for scenarios where the size of the collection may change over time.

• Tuples are immutable and provide a fixed-size collection, making them useful for scenarios where the collection should not be modified.

• Lists and tuples can be used to store and process data such as user information, sensor readings, o...read more

Automate Excel sheet data using Python by using libraries like pandas and openpyxl.

• Use pandas library to read and manipulate Excel data

• Use openpyxl library to create, modify, and save Excel files

• Automate data processing tasks by writing Python scripts to perform desired actions on Excel data

Compute statistics of a given time value in R

• Use lubridate package to work with time data in R

• Calculate summary statistics like mean, median, min, max, and standard deviation

• Convert the time value to a time object before performing calculations

Using R to create two matrices and perform matrix multiplication.

• Create two matrices using matrix() function in R.

• Use %*% operator for matrix multiplication.

• Ensure the dimensions of the matrices are compatible for multiplication.

Right join includes all records from the right table and matching records from the left table, while inner join includes only matching records from both tables.

• Right join keeps all records from the right table, even if there are no matches in the left table.

• Inner join only includes records that have matching values in both tables.

• Example: If we have a table of employees and a table of departments, a right join would include all departments and only the employees that belong t...read more

Standard deviation is a measure of the amount of variation or dispersion of a set of values.

• Standard deviation is calculated as the square root of the variance.

• It indicates how spread out the values in a data set are around the mean.

• A low standard deviation means the values are close to the mean, while a high standard deviation means the values are more spread out.

• For example, in a data set of test scores, a high standard deviation would indicate a wide range of scores, while...read more

Confusion Matrix is a table that is often used to describe the performance of a classification model.

• It is a 2x2 matrix that summarizes the predictions of a classification model.

• It shows the number of true positives, true negatives, false positives, and false negatives.

• It is useful for evaluating the performance of a model by calculating metrics like accuracy, precision, recall, and F1 score.

Calculate probability of unfair coin tossed n times and do hypothesis testing

• Calculate the theoretical probability of getting heads or tails for the unfair coin

• Perform the actual coin toss n times and record the outcomes

• Use hypothesis testing to determine if the coin is unfair based on the observed outcomes

Hyperparameters in random forest are parameters that are set before the learning process begins.

• Hyperparameters control the behavior of the random forest algorithm.

• They are set by the data scientist and are not learned from the data.

• Examples of hyperparameters in random forest include the number of trees, the maximum depth of trees, and the number of features considered at each split.

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

• Pruning helps prevent overfitting by simplifying the model.

• It improves the model's generalization ability by reducing complexity.

• Pruning can be done through pre-pruning or post-pruning.

• Pre-pruning involves setting a threshold to stop tree growth early.

• Post-pruning involves removing branches that do not contribute significantly to accuracy.

• Example: Removing a ...read more

r2 and adjusted r2 are metrics used to evaluate the goodness of fit of a regression model.

• r2 (R-squared) measures the proportion of the variance in the dependent variable that is predictable from the independent variables.

• Adjusted r2 is a modified version of r2 that adjusts for the number of predictors in the model, providing a more accurate assessment of the model's goodness of fit.

• r2 always increases when adding more predictors, while adjusted r2 may decrease if the added p...read more

L1 and L2 regularization are techniques used in machine learning to prevent overfitting by adding penalty terms to the cost function.

• L1 regularization adds the absolute values of the coefficients as penalty term to the cost function.

• L2 regularization adds the squared values of the coefficients as penalty term to the cost function.

• L1 regularization can lead to sparse models by forcing some coefficients to be exactly zero.

• L2 regularization is computationally more efficient comp...read more

I have knowledge of various software tools and programming languages used in data analysis and machine learning.

• Proficient in programming languages such as Python, R, and SQL

• Familiar with data visualization tools like Tableau and Power BI

• Experience with machine learning libraries such as scikit-learn and TensorFlow

Convolutional neural networks (CNNs) are deep learning models specifically designed for processing structured grid data, such as images.

• CNNs use convolutional layers to extract features from input data

• They are commonly used in image recognition tasks, such as object detection and facial recognition

• CNNs are composed of multiple layers, including convolutional, pooling, and fully connected layers

• They are trained using backpropagation and gradient descent algorithms

Handling imbalanced data involves techniques like resampling, using different algorithms, and adjusting class weights.

• Use resampling techniques like oversampling or undersampling to balance the dataset

• Utilize algorithms that are robust to imbalanced data, such as Random Forest, XGBoost, or SVM

• Adjust class weights in the model to give more importance to minority class

KNN is a non-parametric algorithm used for classification and regression tasks.

• KNN stands for K-Nearest Neighbors.

• It works by finding the K closest data points to a given test point.

• The class or value of the test point is then determined by the majority class or average value of the K neighbors.

• KNN can be used for both classification and regression tasks.

• It is a simple and easy-to-understand algorithm, but can be computationally expensive for large datasets.