800+ 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

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

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.

Small change in one dimension causing drastic difference in model output. Explanation and solution.

• This is known as sensitivity to input

• It can be caused by non-linearities in the model or overfitting

• Regularization techniques can be used to reduce sensitivity

• Cross-validation can help identify overfitting

• Ensemble methods can help reduce sensitivity

• It is generally a bad thing as it indicates instability in the model

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

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

The code will output an empty list as a result of slicing from -1 to -5 in the list 'a'.

• Slicing in Python allows you to access a subset of elements in a list or tuple.

• When slicing, the start index is inclusive and the end index is exclusive.

• In this case, a[-1:-5] will result in an empty list because the start index -1 is greater than the end index -5.

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 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')

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 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

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 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.

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

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

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.

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

Data Scientist interview questions on model building, random forest, ROC curve, gradient boosting, and real estate valuation

• For model building, I followed the CRISP-DM process and used various algorithms like logistic regression, decision trees, and random forest

• Random forest hyperparameters include number of trees, maximum depth, minimum samples split, and minimum samples leaf

• ROC curve is a graphical representation of the trade-off between true positive rate and false positi...read more

R square is a statistical measure that represents the proportion of the variance in the dependent variable explained by the independent variables.

• R square is a value between 0 and 1, where 0 indicates that the independent variables do not explain any of the variance in the dependent variable, and 1 indicates that they explain all of it.

• It is used to evaluate the goodness of fit of a regression model.

• Adjusted R square takes into account the number of predictors in the model an...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

Steps to fit a time series model

• Identify the time series pattern

• Choose a suitable model

• Split data into training and testing sets

• Fit the model to the training data

• Evaluate model performance on testing data

• Refine the model if necessary

• Forecast future values using the model

Use pandas' read_csv() method with appropriate parameters to read large .csv files quickly.

• Use the chunksize parameter to read the file in smaller chunks

• Use the low_memory parameter to optimize memory usage

• Use the dtype parameter to specify data types for columns

• Use the usecols parameter to read only necessary columns

• Use the skiprows parameter to skip unnecessary rows

• Use the nrows parameter to read only a specific number of rows

• Use the na_values parameter to specify values to...read more

my_list[5] retrieves the 6th element of the list.

• Indexing starts from 0 in Python.

• The integer inside the square brackets is the index of the element to retrieve.

• If the index is out of range, an IndexError is raised.

Bais-variance tradeoff is the balance between overfitting and underfitting. P values measure the significance of statistical results.

• Bais-variance tradeoff is the tradeoff between the model's ability to fit the training data and its ability to generalize to new data.

• Overfitting occurs when the model is too complex and fits the training data too closely, resulting in poor performance on new data.

• Underfitting occurs when the model is too simple and fails to capture the underlyi...read more

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

The model/approach was chosen based on its accuracy, interpretability, and scalability.

• The chosen model/approach had the highest accuracy compared to others.

• The chosen model/approach was more interpretable and easier to explain to stakeholders.

• The chosen model/approach was more scalable and could handle larger datasets.

• Other models/approaches were considered but did not meet the requirements or had limitations.

• The chosen model/approach was also more suitable for the specific ...read more

Lambda functions are anonymous functions used for short and simple operations. They are different from regular functions in their syntax and usage.

• Lambda functions are defined without a name and keyword 'lambda' is used to define them.

• They can take any number of arguments but can only have one expression.

• They are commonly used in functional programming and as arguments to higher-order functions.

• Lambda functions are often used for short and simple operations that do not requir...read more

To find if a point is inside or outside of a regular polygon, we can use the Ray Casting algorithm.

• Draw a line from the point to a point outside the polygon

• Count the number of times the line intersects with the polygon edges

• If the number of intersections is odd, the point is inside the polygon

• If the number of intersections is even, the point is outside the polygon

Use RAID 5 to store data across all three memory chips with parity bits for fault tolerance.

• Implement RAID 5 to distribute data and parity bits across all three memory chips.

• If one memory chip is corrupted, the data can be reconstructed using the parity bits from the other two chips.

• Example: Store 1GB of data on each chip and use the remaining space for parity bits to ensure fault tolerance.

Avoid ReLU when dealing with negative values or vanishing gradients.

• When dealing with negative values, use Leaky ReLU or ELU instead.

• When facing vanishing gradients, use other activation functions like tanh or sigmoid.

• In some cases, using ReLU in all layers can lead to dead neurons.

• Consider the nature of your data and the problem you are trying to solve before choosing an activation function.

Developed a predictive model to identify potential customer churn for a telecom company

• Identified key factors contributing to customer churn through exploratory data analysis

• Built a logistic regression model to predict customer churn with 85% accuracy

• Provided actionable insights to the business team to reduce customer churn and improve customer retention

• Implemented the model in production environment using Python and SQL

To create dictionaries in Python with repeated keys, use defaultdict from the collections module.

• Import the collections module

• Create a defaultdict object

• Add key-value pairs to the dictionary using the same key multiple times

• Access the values using the key

• Example: from collections import defaultdict; d = defaultdict(list); d['key'].append('value1'); d['key'].append('value2')

To choose optimum probability threshold from ROC, we need to balance between sensitivity and specificity.

• Choose the threshold that maximizes the sum of sensitivity and specificity

• Use Youden's J statistic to find the optimal threshold

• Consider the cost of false positives and false negatives

• Use cross-validation to evaluate the performance of different thresholds

Eigenvalues and eigenvectors are linear algebra concepts used in machine learning for dimensionality reduction and feature extraction.

• Eigenvalues represent the scaling factor of the eigenvectors.

• Eigenvectors are the directions along which a linear transformation acts by stretching or compressing.

• In machine learning, eigenvectors are used for principal component analysis (PCA) to reduce the dimensionality of data.

• Eigenvalues and eigenvectors are also used in image processing f...read more

Optimizers are used to improve the efficiency and accuracy of the training process in machine learning models.

• Optimizers help in finding the optimal set of weights for a given model by minimizing the loss function.

• They use various techniques like momentum, learning rate decay, and adaptive learning rates to speed up the training process.

• Optimizers also prevent the model from getting stuck in local minima and help in generalizing the model to unseen data.

• Examples of optimizers...read more