800+ Data Scientist Interview Questions and Answers

The organization used descriptive and inferential statistics to analyze and build models.

• Descriptive statistics were used to summarize and describe the data, such as mean, median, and standard deviation.

• Inferential statistics were used to make predictions and draw conclusions about the population based on the sample data, such as hypothesis testing and regression analysis.

• The organization may have also used time series analysis, clustering, and classification models.

• Examples ...read more

Code to identify prime numbers between two given numbers.

• Create a function that takes two numbers as input.

• Loop through the range of numbers between the two inputs.

• Check if each number is divisible by any number other than 1 and itself.

• If not, add it to a list of prime numbers.

• Return the list of prime numbers.

Use vectorized operations, avoid loops, and optimize memory usage.

• Use vectorized operations like apply(), map(), and applymap() instead of loops.

• Avoid using iterrows() and itertuples() as they are slower than vectorized operations.

• Optimize memory usage by using appropriate data types and dropping unnecessary columns.

• Use inplace=True parameter to modify the DataFrame in place instead of creating a copy.

• Use the pd.eval() function to perform arithmetic operations on large DataFr...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

XgBoost algorithm uses a greedy approach to determine splits based on feature importance.

• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the highest information gain, the data will be split based on di...read more

PCA is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.

• PCA can be used for feature extraction, data visualization, and noise reduction.

• PCA cannot be used for causal inference or to handle missing data.

• PCA assumes linear relationships between variables and may not work well with non-linear data.

• PCA can be applied to various fields such as finance, image processing, and genetics.

Blue score is not a term used in regression analysis.

• Blue score is not a standard term in regression analysis

• It is possible that the interviewer meant to ask about another metric such as R-squared or mean squared error

• Without further context, it is difficult to provide a more specific answer

Multicollinearity in logistic regression can be checked using correlation matrix and variance inflation factor (VIF).

• Calculate the correlation matrix of the independent variables and check for high correlation coefficients.

• Calculate the VIF for each independent variable and check for values greater than 5 or 10.

• Consider removing one of the highly correlated variables or variables with high VIF to address multicollinearity.

• Example: If variables A and B have a correlation coeff...read more

My research topics focus on developing scalable machine learning models for predictive analytics in finance.

• I have researched and implemented various machine learning algorithms such as random forests, gradient boosting, and neural networks.

• I have explored techniques for feature engineering and model optimization to improve scalability and performance.

• I have chosen specific models based on their ability to handle large datasets and complex relationships within financial data....read more

Estimating the number of WhatsApp users worldwide requires a combination of data sources and statistical methods.

• Collect data from WhatsApp's official reports and announcements

• Use third-party analytics tools to estimate user numbers

• Analyze demographic and geographic trends to extrapolate global user numbers

• Consider factors such as population growth and smartphone adoption rates

• Compare with similar messaging apps to validate estimates

Logistic Regression is a statistical method used to model the probability of a binary outcome.

• Logistic Regression is used when the dependent variable is binary (e.g., 0 or 1, Yes or No).

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

• Assumptions of linear regression include linearity, independence of errors, homoscedasticity, and normality of errors.

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.

Group by is used to group data based on a column while window function is used to perform calculations on a specific window of data.

• Group by is used to aggregate data based on a specific column

• Window function is used to perform calculations on a specific window of data

• Group by is used with aggregate functions like sum, count, avg, etc.

• Window function is used with analytical functions like rank, lead, lag, etc.

• Group by creates a new table with aggregated data while window func...read more

To test time series trend break up, statistical tests like Augmented Dickey-Fuller test can be used.

• Augmented Dickey-Fuller test can be used to check if a time series is stationary or not.

• If the time series is not stationary, we can use differencing to make it stationary.

• After differencing, we can again perform the Augmented Dickey-Fuller test to check for stationarity.

• If there is a significant change in the mean or variance of the time series, we can use change point detecti...read more

Object detection using CNN involves training a neural network to identify and locate objects within an image.

• CNNs use convolutional layers to extract features from images

• These features are then passed through fully connected layers to classify and locate objects

• Common architectures for object detection include YOLO, SSD, and Faster R-CNN

Inner join combines rows from two tables based on a related column between them.

• Inner join returns only the rows where there is a match between the columns in both tables

• Null values in the columns being joined will not affect the inner join result

• Blank values or non-matching values will not be included in the inner join result

Use a regression algorithm like linear regression or decision tree regression.

• Consider using linear regression if the relationship between variables is linear.

• Decision tree regression can handle non-linear relationships between variables.

• Evaluate the performance of different algorithms using cross-validation.

• Consider the interpretability of the model when choosing an algorithm.

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.

We minimize the loss in logistic regression.

• The goal of logistic regression is to minimize the loss function.

• The loss function measures the difference between predicted and actual values.

• The optimization algorithm tries to find the values of coefficients that minimize the loss function.

• Minimizing the loss function leads to better model performance.

• Examples of loss functions used in logistic regression are cross-entropy and log loss.

Time series classification involves using machine learning algorithms to classify time series data based on patterns and trends.

• Preprocess the time series data by removing noise and outliers

• Extract features from the time series data using techniques such as Fourier transforms or wavelet transforms

• Train a machine learning algorithm such as a decision tree or neural network on the extracted features

• Evaluate the performance of the algorithm using metrics such as accuracy or F1 s...read more

P-values are a statistical measure that helps determine the likelihood of obtaining a result by chance.

• P-values range from 0 to 1, with a smaller value indicating stronger evidence against the null hypothesis.

• A p-value of 0.05 or less is typically considered statistically significant.

• P-values are commonly used in hypothesis testing to determine if a result is statistically significant or not.

A logarithm is a mathematical function that measures the relationship between two quantities.

• Logarithms are used to simplify complex calculations involving large numbers.

• They are used in linear algebra to transform multiplicative relationships into additive ones.

• Logarithms are also used in data analysis to transform skewed data into a more normal distribution.

• Common logarithms use base 10, while natural logarithms use base e (approximately 2.718).

SQL query to find date ids with higher temperature compared to previous dates in weather table

• Use self join to compare temperature of current date with previous dates

• Order the table by date to ensure correct comparison

• Select date ids where temperature is higher than previous dates

I am very familiar with Python, SQL, and ML models.

• I have extensive experience using Python for data analysis and machine learning tasks.

• I am proficient in writing SQL queries to extract data from databases.

• I have worked with a variety of ML models, including regression, classification, and clustering.

• I am familiar with popular ML libraries such as scikit-learn, TensorFlow, and Keras.

• I have experience with data preprocessing, feature engineering, and model evaluation.

• I am con...read more

I used a combination of supervised and unsupervised learning approaches to analyze the data.

• I used supervised learning to train models for classification and regression tasks.

• I used unsupervised learning to identify patterns and relationships in the data.

• I also used feature engineering to extract relevant features from the data.

• I chose this approach because it allowed me to gain insights from the data and make predictions based on it.

Disadvantages of logistic regression

• Assumes linearity between independent variables and log odds of the dependent variable

• Prone to overfitting with large number of features

• Not suitable for complex relationships or non-linear data

• Can't handle missing values well

Overfitting and underfitting are common problems in machine learning where the model either learns the noise in the training data or fails to capture the underlying patterns.

• Overfitting occurs when a model learns the training data too well, including the noise and outliers, leading to poor generalization on new data.

• Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in high bias and low variance.

• Solutions to overfitting i...read more

Use dilated convolutions to increase receptive field without increasing computational cost.

• Utilize dilated convolutions to expand the receptive field without adding extra parameters or computations.

• Increase the dilation rate in convolutional layers to capture information from a larger area without increasing the number of parameters.

• Dilated convolutions allow for larger receptive fields while maintaining the same computational cost.

• Example: In image segmentation tasks, dilate...read more

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

Network errors in cellular technology are resolved through error detection, correction codes, retransmission, and handover techniques.

• Error detection techniques like CRC (Cyclic Redundancy Check) are used to identify errors in data transmission.

• Error correction codes like Reed-Solomon codes are employed to correct errors in the received data.

• Retransmission of data packets is done when errors are detected, ensuring accurate delivery.

• Handover techniques allow seamless transfer ...read more

Decision tree algorithm is a tree-like model used for classification and regression. Cross entropy is a measure of the difference between two probability distributions.

• Decision tree algorithm recursively splits the data into subsets based on the most significant attribute until a stopping criterion is met.

• It is a popular algorithm for both classification and regression tasks.

• Cross entropy is used as a loss function in machine learning to measure the difference between predict...read more

Machine Learning is a subset of Artificial Intelligence that uses algorithms to learn from data and make predictions.

• AI (Artificial Intelligence) is the broader concept of machines being able to carry out tasks in a way that we would consider 'smart'.

• ML (Machine Learning) is a subset of AI that focuses on the development of computer programs that can access data and use it to learn for themselves.

• DL (Deep Learning) is a subset of ML that uses neural networks with many layers ...read more

Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.

• Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.

• LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.

• The geometric interpretation of Ridge is that it adds a constraint to the size of the coefficients, which shrinks them towards the origi...read more

Backpropagation is a supervised learning algorithm used to train neural networks by adjusting weights to minimize error.

• It involves propagating the error backwards through the network to adjust the weights of the connections between neurons.

• The algorithm uses the chain rule of calculus to calculate the gradient of the error with respect to each weight.

• The weights are then updated using a learning rate and the calculated gradient.

• This process is repeated for multiple iteration...read more

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

PCA stands for Principal Component Analysis. It is a statistical technique used for dimensionality reduction.

• PCA is used to reduce the number of variables in a dataset while retaining the maximum amount of information.

• It is commonly used in data preprocessing and exploratory data analysis.

• PCA is also used in image processing, speech recognition, and finance.

• It works by transforming the original variables into a new set of uncorrelated variables called principal components.

• The...read more

Best practices for handling large data sets include data preprocessing, using distributed computing frameworks, and optimizing storage and retrieval methods.

• Perform data preprocessing to clean and transform data before analysis.

• Utilize distributed computing frameworks like Hadoop or Spark for parallel processing.

• Optimize storage and retrieval methods by using efficient data structures and indexing.

• Consider using cloud services for scalable storage and processing capabilities....read more

A confusion matrix is a table that is used to describe the performance of a classification model.

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

• It helps in evaluating the performance of a machine learning model by providing insights into the model's accuracy, precision, recall, and F1 score.

• It is particularly useful in scenarios where class imbalance exists or when different misclassification costs are involved.

• Example: In a binary...read more

Low CPI may indicate inefficiencies in marketing strategies or poor product quality.

• Low CPI can be caused by ineffective targeting of ads to the right audience.

• It can also be due to poor product quality or lack of demand for the product.

• Low CPI may require a reassessment of marketing strategies and product offerings.

• Examples of low CPI include unsuccessful ad campaigns or low sales numbers.

• Improving CPI can lead to increased revenue and profitability for the company.

I will start by understanding the data source and its quality, then clean and preprocess the data before performing exploratory data analysis.

• Understand the data source and its quality

• Clean and preprocess the data

• Perform exploratory data analysis

• Identify patterns and trends in the data

• Use statistical methods to analyze the data

• Visualize the data using graphs and charts

• Iterate and refine the analysis as needed

Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.

• The theorem applies to large sample sizes.

• It is a fundamental concept in statistics.

• It is used to estimate population parameters from sample statistics.

• It is important in hypothesis testing and confidence intervals.

• Example: If we take a large number of samples of the same size from a population, the distribution of the sample means will b...read more

To maintain data integrity and generalization, use techniques like data cleaning, normalization, and feature engineering.

• Perform data cleaning to remove errors, duplicates, and inconsistencies.

• Normalize data to ensure consistency and comparability.

• Utilize feature engineering to create new features or transform existing ones for better model performance.

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

I have conducted research in machine learning and natural language processing, and I would approach problems by first understanding the data and then applying appropriate algorithms.

• Conducted research in machine learning and natural language processing

• Approach problems by understanding the data first

• Apply appropriate algorithms based on the problem

• Utilize data visualization techniques to gain insights

Decision tree is a tree-like model used for classification and regression. OpenCV parameters include image processing and feature detection.

• Decision tree is a supervised learning algorithm that recursively splits the data into subsets based on the most significant attribute.

• It is used for both classification and regression tasks.

• OpenCV parameters include image processing techniques like smoothing, thresholding, and morphological operations.

• Feature detection parameters include...read more

Measures to check performance of classification model

• Accuracy

• Precision

• Recall

• F1 Score

• ROC Curve

• Confusion Matrix