BT Group Business Intelligence Developer Interview Questions and Answers

Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Q1. SQL,Aptitude Questions
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Q2. Logical Questions,case studies
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Q1. What is the difference between linear and classification?
Ans. 
Linear regression is used for predicting continuous values, while classification is used for predicting discrete values.
• Linear regression is used when the output variable is continuous, such as predicting house prices based on features like size and location.

• Classification is used when the output variable is categorical, such as predicting whether an email is spam or not based on its content.

• Linear regression aims to f...read more
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Q2. Explain example of outlier
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An outlier is a data point that differs significantly from other data points in a dataset.
• Outliers can skew statistical analyses and machine learning models.

• Examples of outliers include a person's weight being recorded as 1000 lbs, when the average weight is around 150 lbs.

• Outliers can be detected using statistical methods like Z-score or IQR.
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Q3. Explain k mean algorithm
Ans. 
K-means algorithm is a clustering technique that partitions data into k clusters based on similarity.
• Divides data points into k clusters based on centroids

• Iteratively assigns data points to the nearest centroid and updates centroids

• Continues until centroids no longer change significantly

• Example: Grouping customers based on purchasing behavior
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Q4. Explain the metrics for classification
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Classification metrics are used to evaluate the performance of a classification model.
• Accuracy: measures the proportion of correctly classified instances out of total instances

• Precision: measures the proportion of true positive predictions out of all positive predictions

• Recall: measures the proportion of true positive predictions out of all actual positive instances

• F1 Score: harmonic mean of precision and recall, balan...read more
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Q1. Exptected salary
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Q2. What do you expect for work
Ans. 
I expect challenging projects, opportunities for growth, collaborative team environment, and work-life balance.
• Challenging projects that allow me to apply my data science skills and learn new techniques

• Opportunities for growth and advancement within the company

• Collaborative team environment where I can share ideas and work together towards common goals

• Work-life balance to ensure I can perform at my best both profession
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Topic was joining and calculating

Q1. Asked about previous work experience
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Q2. Explain about your skills
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Q1. Questions on what you have worked on till now
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Power BI case study to see the expertise in Power BI

Q1. What is the difference between AI, deep learning and machine learning
Ans. 
AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart. Machine learning is a subset of AI that allows machines to learn from data. Deep learning is a subset of machine learning that uses neural networks with many layers to model and solve complex problems.
• AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart.

• Machine ...read more
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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