1000+ Data Scientist Interview Questions and Answers

To find a line that best fits the data with 1000 samples and 700 dimensions, we can use linear regression.

• For unsupervised ML approach, we can use Principal Component Analysis (PCA) to reduce dimensions and then fit a line using linear regression.

• For supervised ML approach, we need to select a target column. We can choose any of the 700 dimensions as the target and treat it as a regression problem.

• Potential problems of treating this as a supervised problem include: lack of in...read more

Calculate the total sum of array elements until a single-digit number is obtained by repeatedly summing digits.

• Iterate through the array and calculate the sum of all elements.

• If the sum is a single-digit number, return it. Otherwise, repeat the process of summing digits until a single-digit number is obtained.

• Return the final single-digit sum.

Retrieve top 2 cities per state based on max number in third column of pandas dataframe.

• Group the dataframe by state column

• Sort each group by the third column in descending order

• Retrieve the top 2 rows of each group using head(2) function

• Concatenate the resulting dataframes using pd.concat() function

Answering a question on data and objective function for cost and revenue optimization case studies.

• For cost optimization, look at data related to expenses, production costs, and resource allocation.

• For revenue optimization, look at data related to sales, customer behavior, and market trends.

• Objective function for cost optimization could be minimizing expenses while maintaining quality.

• Objective function for revenue optimization could be maximizing profits while satisfying cus...read more

Finding index of 2 numbers having total equal to target in a list without nested for loop.

• Use dictionary to store the difference between target and each element of list.

• Iterate through list and check if element is in dictionary.

• Return the indices of the two elements that add up to target.

One way to measure model effectiveness without using confusion matrix metrics is by using area under the receiver operating characteristic curve (AUC-ROC).

• Calculate the AUC-ROC score to evaluate the model's ability to distinguish between positive and negative classes.

• AUC-ROC considers the entire range of classification thresholds and is insensitive to class imbalance.

• Higher AUC-ROC score indicates better model performance.

• Example: A model with an AUC-ROC score of 0.85 perform...read more

Tokenization in NLP is the process of breaking down text into smaller units called tokens.

• Tokenization is a fundamental step in NLP for text preprocessing.

• Tokens can be words, phrases, or even individual characters.

• Tokenization helps in preparing text data for further analysis or modeling.

• NLTK tokenizers provide additional functionalities like handling contractions, punctuation, etc.

• str.split() may not handle complex tokenization scenarios as effectively as NLTK tokenizers.

The direction of fluid flow in a vertical pipe depends on the pressure difference between two vertical locations.

• Fluid flows from high pressure to low pressure.

• If the pressure at the lower location is higher than the pressure at the upper location, the fluid will flow downwards.

• If the pressure at the upper location is higher than the pressure at the lower location, the fluid will flow upwards.

• The magnitude of the pressure difference determines the rate of fluid flow.

Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

• For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.

• For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.

• For SVM, important hyperparameters to tune include kernel type, regularization parameter, and gamma value.

• Grid ...read more

Use NLTK's MWETokenizer to preserve specific word pairs or triplets during tokenization.

• MWETokenizer allows you to define multi-word expressions (MWEs) that should be treated as single tokens.

• Example: If you define MWE as ('first', 'name'), the tokenizer will keep 'first name' together.

• You can add multiple MWEs, such as ('New', 'York') and ('data', 'science').

• This is particularly useful in NLP tasks where context matters, like sentiment analysis.

To retain special characters in pandas data, use encoding parameter while reading the data.

• Use encoding parameter while reading the data in pandas

• Specify the encoding type of the data file

• Example: pd.read_csv('filename.csv', encoding='utf-8')

There are several types of ML algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

• Supervised learning: algorithms learn from labeled data to make predictions or classifications (e.g., linear regression, decision trees)

• Unsupervised learning: algorithms find patterns or relationships in unlabeled data (e.g., clustering, dimensionality reduction)

• Reinforcement learning: algorithms learn through trial and error by interacting with an enviro...read more

Read sample data in text, check for null and unique values, create new column by multiplying two features

• Save text data as CSV and read in Python using pandas

• Use isnull() to check for null values

• Use nunique() to check for unique values

• Create a new column by multiplying two existing columns

• Add the new column to the existing dataframe

To get embeddings of long sentences/paragraphs truncated by BERT, we can use pooling techniques like mean/max pooling.

• We can use pooling techniques like mean/max pooling to get embeddings of truncated sentences/paragraphs.

• We can also use sliding window approach to get embeddings of overlapping segments of the long input.

• For using BERT on such long inputs, we can use sentence embeddings or word embeddings depending on the task.

• Models like Longformer and Reformer can handle lon...read more

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

Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.

• Use grid search to exhaustively search through a specified parameter grid

• Utilize random search to randomly sample hyperparameters from a specified distribution

• Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations

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.

To prevent overfitting, I used techniques like regularization, cross-validation, and early stopping. For underfitting, I tried increasing model complexity and adding more features.

• Used regularization techniques like L1 and L2 regularization to penalize large weights

• Used cross-validation to evaluate model performance on different subsets of data

• Used early stopping to prevent the model from continuing to train when performance on validation set stops improving

• For underfitting, ...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

Estimate the number of white cars using surveys, traffic data, and image recognition techniques.

• Conduct surveys: Ask residents about car colors in their neighborhoods.

• Use traffic cameras: Analyze footage to count white cars during peak hours.

• Leverage social media: Analyze posts or images of cars in the city.

• Utilize machine learning: Train a model on images of cars to identify white ones.

• Collaborate with local authorities: Access registration data for car colors.

The Wald test is used in logistic regression to check the significance of the variable.

• The Wald test calculates the ratio of the estimated coefficient to its standard error.

• It follows a chi-square distribution with one degree of freedom.

• A small p-value indicates that the variable is significant.

• For example, in Python, the statsmodels library provides the Wald test in the summary of a logistic regression model.

Answering questions related to data science concepts and techniques.

• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different subsets of the data and averaging their predictions. Boost...read more

Hyperparameters are settings that control the behavior of machine learning algorithms.

• Hyperparameters are set before training the model.

• They control the learning process and affect the model's performance.

• Examples include learning rate, regularization strength, and number of hidden layers.

• Optimizing hyperparameters is important for achieving better model accuracy.

Communicate transparently and offer alternative solutions.

• Explain the limitations of the available data and the potential risks of making decisions based on incomplete information.

• Offer alternative solutions that can be implemented with the available data.

• Collaborate with the customer to identify additional data sources or explore other options to gather more data.

• Provide regular updates on the progress of data collection and analysis.

• Ensure that all decisions are based on so...read more

Hyperparameters in XGBoost algorithm control the behavior of the model during training.

• Hyperparameters include parameters like learning rate, max depth, number of trees, etc.

• They are set before the training process and can greatly impact the model's performance.

• Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100

The resultant table will have all the columns from both tables and the rows will be a combination of matching rows.

• The resultant table will have all the columns from both tables

• The rows in the resultant table will be a combination of matching rows

• If the second table has repeated keys, there will be multiple rows with the same key in the resultant table

Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.

• Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).

• LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).

• Ridge regression tends to shrink the coefficients towards zero but does not set them e...read more