Nokia Data Analyst Interview Questions and Answers

Q1. It was an easy situational ques
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Q2. It was an easy situational questiokn
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Q1. Same as the first one
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Q2. Same as the previous round
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Q1. Current roles and responsibilities, Few queries to write, few questions related to DWH and what type of role you have done in past etc
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Q1. Managerial round mostly questions like what challenges did you face, how did you overcome on that, what was the major success / appreciation you got
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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. SQL joins and simple query
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Q2. Phython basics and simle data fram
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Q3. Hr round only formality
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Q1. Good interview with no mistakes.
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Q2. Why you want to work with us ?
Ans. 
I want to work with you because of your reputation in the industry and the exciting opportunities for growth and learning.
• I am impressed by your company's reputation in the industry and the positive feedback I have heard from current and former employees.

• Your organization offers a dynamic and challenging work environment, which aligns with my career goals and aspirations.

• I am excited about the opportunity to work with ...read more
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SQL query to find the top 5 employee having the max salary

Q1. If a dataset has missing values, how would you handle them?
Ans. 
I would handle missing values by imputing them using methods like mean, median, mode, or predictive modeling.
• Identify the extent of missing data in the dataset

• Consider the type of missing values (completely at random, missing at random, or not missing at random)

• Impute missing values using mean, median, mode, predictive modeling, or other statistical methods

• Evaluate the impact of imputation on the dataset's integrity an
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Q2. How would you analyze sales data to find ways to increase revenue?
Ans. 
I would analyze sales data by identifying trends, customer behavior, and market opportunities to increase revenue.
• Identify key performance indicators (KPIs) such as average order value, conversion rate, and customer acquisition cost.

• Segment customers based on demographics, purchase history, and behavior to target specific groups with personalized marketing strategies.

• Analyze sales channels to determine which ones are m...read more
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Q1. Qualification of my degree
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Q2. My personal skills for company
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