Trainee Decision Scientist Interview Questions and Answers for Freshers

Before Excel, data was stored and analyzed using manual methods.

• Data was stored in physical files such as paper documents or index cards.

• Analysis was done manually using tools such as calculators or statistical tables.

• Data was often organized using charts or graphs.

• Data entry was done by hand, which was time-consuming and prone to errors.

Ticket price for a play in an auditorium

• Analyze the production cost and expected revenue

• Consider the target audience and their willingness to pay

• Research the ticket prices of similar plays in the area

• Offer discounts for early bird or group bookings

• Adjust the price based on demand and availability

Power BI and Tableau are used for data visualization and business intelligence purposes.

• They help in creating interactive dashboards and reports.

• They allow users to analyze and explore data in a visual format.

• They can connect to various data sources and transform data for analysis.

• They provide features like data filtering, sorting, and aggregation.

• They enable users to share insights and collaborate with others.

• Example: Power BI can be used to create a sales dashboard that sho...read more

Random forest is an ensemble learning method that uses multiple decision trees to make predictions, while a decision tree is a single tree-like structure that makes decisions based on features.

• Random forest is a collection of decision trees that work together to make predictions.

• Decision tree is a single tree-like structure that makes decisions based on features.

• Random forest reduces overfitting by averaging the predictions of multiple trees.

• Decision tree can be prone to over...read more

My approach to a problem statement involves understanding the problem, gathering data, analyzing the data, and developing a solution.

• Understand the problem statement thoroughly

• Gather relevant data and information

• Analyze the data to identify patterns and insights

• Develop a solution based on the analysis

• Test and refine the solution as needed

Developed a predictive model for customer churn in a telecom company

• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest

• Evaluated model performance using metrics like accuracy and AUC-ROC curve