Zypp Electric Business Intelligence Analyst Interview Questions and Answers

Q1. Explain Joins in SQL?
Ans. 
Joins in SQL are used to combine rows from two or more tables based on a related column between them.
• Joins are used to retrieve data from multiple tables based on a related column between them

• Types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• INNER JOIN returns rows when there is at least one match in both tables

• LEFT JOIN returns all rows from the left table and the matched rows from the right table

• ...read more
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Q2. What is Normalisation?
Ans. 
Normalisation is the process of organizing data in a database to reduce redundancy and improve data integrity.
• Normalisation involves breaking down data into smaller, more manageable tables

• It helps in reducing data redundancy by storing data in a structured way

• It improves data integrity by ensuring that each piece of data is stored in only one place

• Normalization is usually done in multiple stages (1NF, 2NF, 3NF, etc.) t...read more
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Questions mostly revolved around pandas and numpy.

Q1. Explain about yourself
Ans. 
I am a data analyst with a strong background in statistics and data visualization.
• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience in analyzing large datasets and creating data visualizations

• Strong problem-solving skills and attention to detail
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Q2. What is ur experience on Data analyst
Ans. 
I have 3 years of experience as a Data Analyst in the retail industry.
• Utilized SQL to extract and analyze data from databases

• Created visualizations using Tableau to present insights to stakeholders

• Performed statistical analysis to identify trends and patterns in sales data
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Q1. Explain the experience in data
Ans. 
I have over 5 years of experience in data analysis, including working with large datasets, creating visualizations, and providing actionable insights.
• Experience in cleaning and transforming data to ensure accuracy and consistency

• Proficient in using statistical tools and techniques to analyze data

• Ability to create data visualizations and dashboards to communicate findings

• Experience in identifying trends and patterns in ...read more
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Q2. Explain the courses done in data analyst
Ans. 
Courses in data analyst cover topics such as statistics, data visualization, machine learning, and data mining.
• Statistics: Understanding of statistical concepts and techniques for data analysis.

• Data Visualization: Creating visual representations of data to aid in analysis and decision-making.

• Machine Learning: Using algorithms and models to analyze and make predictions based on data.

• Data Mining: Extracting patterns and ...read more
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Q1. Projects Business case study
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Q1. Family details Pressure questions
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Normal CAT level questions

Q1. Asked some guesstimates and some SQL
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Q1. Based on my resume and my previous company and Statistics
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Q1. Related to Statistics
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Q1. Salary negotiation.
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Q1. Tell about yourself
Ans. 
I am a data analyst with a strong background in statistics and data visualization.
• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience in analyzing large datasets and creating data visualizations

• Strong problem-solving skills
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Q2. What are the steps you do for Data cleaning
Ans. 
Data cleaning involves removing or correcting errors in a dataset to ensure accuracy and consistency.
• Remove duplicate entries

• Fill in missing values

• Correct formatting errors

• Identify and remove outliers

• Standardize data types
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Technical + Analytics
I gave my coding test this august 2024, it was purely based on SQL coding
8 question, 3 easy, 3 intermediate and 3 hard.

Q1. Introduction and explain any one of the projects plus what was the analytics you used
Ans. 
Implemented a project to optimize marketing campaigns using predictive analytics
• Developed a predictive model to identify the most effective marketing channels

• Utilized data from past campaigns to train the model

• Used techniques such as regression analysis and decision trees to analyze the data

• Generated insights on customer behavior and preferences to improve campaign targeting
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Q2. SQL coding, shared screen and was asked to find cumulative sum of cost for a table.
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