Barclays Data Scientist Interview Questions and Answers

Hard level aptitude questions... Time taking sums

Q1. Puzzles and probability questions
Add your answer
Q2. Asking completely about data science projects in your resume
Add your answer

Q1. Just asked some puzzle questions
Add your answer
Q2. Asked if iam willing to relocate and about my family
Add your answer

Aptitude test for about an hour.

Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
Add your answer
Q2. What are parameters used in a random forest.
Ans. 
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
• Number of trees: The number of decision trees to be used in the random forest.

• Maximum depth of trees: The maximum depth allowed for each decision tree.

• Minimum samples split: The minimum number of samples required to split a node.

• Maximum features: The maximum number of features to consider when
Answered by AI
Add your answer

Q1. Metrics and related questions
Add your answer

Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test

Q1. Questions on ML techniques and practices, how to handle large data in python, lots of logical questions and handling overfitting, underfitting, etc in model building.
Add your answer

Q1. What do you do if your model has higher false positives
Ans. 
Investigate the model performance metrics and adjust the threshold for classification.
• Analyze the confusion matrix to understand the distribution of false positives.

• Adjust the threshold for classification to reduce false positives.

• Consider using different evaluation metrics like precision, recall, and F1 score.

• Explore feature importance to identify variables contributing to false positives.
Answered by AI
Add your answer

Q1. Machine learning algorithms.
Ans. 
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
Answered by AI
Add your answer
Q2. Credit risk life cycle
Add your answer
Q3. Pandas related questions
Add your answer

Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
Answered by AI
Add your answer
Q2. Pandas related questions
Add your answer
Q3. Bagging and Boosting
Add your answer

Q1. Explain AIC and BIC
Ans. 
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
Answered by AI
Add your answer
Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
Answered by AI
Add your answer
Q3. Bagging and boosting
Add your answer

Coding related objective questions

Q1. Basic questions based on cv. Questions based on python,SQL,ML algorithms
Add your answer

Q1. Explain previous projects
Add your answer
Q2. Explain 2 algo of your choice
Add your answer