Dun & Bradstreet Data Scientist Interview Questions and Answers

Q1. Write a few python queries and some live sql coding and pyspark coding
Add your answer
Q2. Machine learning questions. Way too deep. which is not even required
Add your answer

Q1. How to treat multicollinearity, imbalanced dataset
Ans. 
Multicollinearity can be treated by using techniques like feature selection, PCA, or regularization. Imbalanced datasets can be addressed by resampling techniques like oversampling or undersampling.
• For multicollinearity, consider using techniques like feature selection to remove redundant variables, PCA to reduce dimensionality, or regularization like Lasso or Ridge regression.

• For imbalanced datasets, try resampling te...read more
Answered by AI
Add your answer

Q1. Logistic regression in detail
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given observation belongs to a particular category.

• The output of logistic regression is a probability score between 0 and 1.

• It uses the logistic function...read more
Answered by AI
Add your answer
Q2. Project related questions based on Machine learning
Add your answer

Q1. SQL basics Python Basics Linux Basics
Add your answer
Q2. Questions on date and time and joins.
Add your answer

Q1. Tell me about yourself
Ans. 
I am a data analyst with a strong background in statistics and programming.
• Experienced in analyzing large datasets and extracting meaningful insights

• Proficient in statistical analysis and data visualization tools such as Python, R, and Tableau

• Skilled in SQL for data querying and manipulation

• Strong problem-solving and critical thinking abilities

• Effective communication skills for presenting findings to stakeholders
Answered by AI
Add your answer
Q2. Projects you have done
Ans. 
Developed a predictive model to forecast customer churn for a telecommunications company.
• Used machine learning algorithms to analyze customer data

• Performed feature engineering to identify key predictors of churn

• Built and trained a model using historical data

• Evaluated model performance and provided actionable insights to reduce churn
Answered by AI
Add your answer

Q1. Baisc questions, communication skills with basic conputer knowledge and much about previous employment skills
Add your answer
Q2. Same basic questions were asked in 2nd round
Add your answer

Q1. Do you know about INDas?
Ans. 
INDas stands for Indian Accounting Standards, which are a set of accounting standards notified by the Ministry of Corporate Affairs in India.
• INDas are based on International Financial Reporting Standards (IFRS) to ensure transparency and consistency in financial reporting.

• They are mandatory for certain classes of companies in India, such as listed companies and certain other public companies.

• INDas aim to improve the qu...read more
Answered by AI
Add your answer

Q1. How do you start with a bottom up research?
Ans. 
Start bottom up research by identifying specific data points, analyzing them individually, and then synthesizing them into a larger conclusion.
• Identify specific data points or pieces of information relevant to the research topic

• Analyze each data point individually to understand its significance and implications

• Synthesize the individual data points into a larger conclusion or insight

• Use a structured approach to organize...read more
Answered by AI
Add your answer
Q2. By analysing is,bs and cf. Reading investor presentation and annual report
Add your answer

There was around 6-7 query to write

Requires good SQL query skills