IBM Analytics Manager Interview Questions and Answers

Q1. Technical aspects of the job
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Q1. More general questions
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Q2. Some hypothetical situation will be given to see your approach
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Q1. Technical round to see the fit for the required skill.
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Q1. Resume screening Self introduction Roles and responsibilities
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Given task Statics standard deviations Attrition Average of given table values and Given graph economi graph and poverty graph base on that need to gave answers 30 qustion and 60 min time duration

Q1. Versant test should be need to attend Hirepro will have interview prosess in YouTube will check and prepare mid level hard
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Q2. We can goo for online prosess how it is
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Q1. Manger Level round they will ask KPI NPS AHT standard deviations Service Level handling Root cause Analysis Weighted average Same product how will calculate Totall Swer % how will get Many of like bpi <20 ...read more
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Q1. Salary discussion and update structure of our role and responsibilities
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
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Q2. Parametric vs non parametruc model
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Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
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Q1. Tell me about your self?
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Q2. What is maths and stats
Ans. 
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
• Maths involves the study of numbers, quantities, shapes, and patterns.

• Stats involves collecting, analyzing, interpreting, and presenting data.

• Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

• Stats is used to make informed decisions, draw conclusions, and test hypotheses.

• Both ma...read more
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Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Time vs money, time time time

Dsa, python sql etl ssis

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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Q1. About your project and experience
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Core Python questions were asked

Q1. How tfidf works in NLP
Ans. 
TF-IDF is a statistical measure used to evaluate the importance of a word in a document.
• TF-IDF stands for Term Frequency-Inverse Document Frequency

• It is used to weigh a word's importance in a document by considering its frequency in the document and across all documents

• The formula for TF-IDF is: TF-IDF = TF * IDF

• TF (Term Frequency) measures how frequently a term appears in a document

• IDF (Inverse Document Frequency) mea...read more
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Q2. What is the difference between group by and window function in sql
Ans. 
Group by is used to group data based on a column while window function is used to perform calculations on a specific window of data.
• Group by is used to aggregate data based on a specific column

• Window function is used to perform calculations on a specific window of data

• Group by is used with aggregate functions like sum, count, avg, etc.

• Window function is used with analytical functions like rank, lead, lag, etc.

• Group by ...read more
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Q1. Tell me about the project you worked on 4 years back
Ans. 
Developed a predictive model to forecast customer churn for a telecom company.
• Used machine learning algorithms like logistic regression and random forest.

• Preprocessed and cleaned the dataset by handling missing values and outliers.

• Performed feature engineering to create new variables for better model performance.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model in prod
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Q2. Why are you leaving your current company
Ans. 
Seeking new challenges and opportunities for growth.
• Looking for a more challenging role that aligns with my career goals.

• Seeking a company that values innovation and encourages professional development.

• Want to work in a more collaborative and diverse team environment.

• Desire to explore new technologies and industries.

• Current company lacks opportunities for advancement or career growth.
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