Bharti Airtel Senior Data Analyst Interview Questions and Answers

Q1. Questions were based on DSA and C
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Q2. It was basically a coding round with simple logic
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Average type questions.

Q1. About the domain/fields or have any experienced or what u did there
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Q1. Self introduction
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Q1. Isolatn forest work? evalution metrics in laymann tems , pyspark basics , job lib
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Q1. Scenerio based data science questns , paramatric and non parametric data
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Q1. Can you automate dashobaord on excel on daily routine, Do you know proper formulation of dashboard.
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Q2. How many years experience do you have in sql. Please provide solution for some sql technical questions. Or can you properly provide adhoc requirement by urgently analysing on compony database. There was s...read more
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Q1. Basic ML questions were asked.
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Q1. All technical questions related to profile
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Q1. Data science questions
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Q1. Current role and experience related
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Q1. Technical questions
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Q2. Data science questions by onshore team
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Q1. Salary joining date
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Q2. Expected salary joining date
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There was a question which say, write the string in the reverse order.

Q1. What are your strengths and weakness.
Ans. 
My strengths include strong analytical skills and attention to detail. My weakness is that I can be overly critical of my own work.
• Strength: Strong analytical skills

• Strength: Attention to detail

• Weakness: Overly critical of my own work
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Q2. Tell me about yourself
Ans. 
I am a data analyst with experience in analyzing and interpreting complex data sets to drive business insights.
• Proficient in SQL, Python, and Excel for data analysis

• Skilled in data visualization using Tableau and Power BI

• Experience in conducting statistical analysis and creating predictive models

• Strong problem-solving and critical thinking skills

• Effective communication and presentation skills

• Ability to work collaborati
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Q1. Basic SQL, Basic questions around discriptive stats and ML concepts. Questions around previous projects
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Q1. Basic Stats and ML questions, questions around linear models, outliers handling. End to end model pipeline for scinario.
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Simple EDA and model building on toy data.

Q1. Managers access story telling and articulation skills.
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