Lithium Urban Technologies HR Executive 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?
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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.

Sql test in Hackerrank

Q1. Questions on SQL was asked
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Q1. Managerial round. I was asked questions on case study for captain acquisition
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Apptitude and technical mcqs

Q1. Sql queries and the projects that i have done
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Q1. Normal hr round
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Test duration of 1 hour based on probability and control statements

Q1. Ml algorithms that you have prior knowledge about
Ans. 
I have prior knowledge about various machine learning algorithms.
• Linear Regression

• Logistic Regression

• Decision Trees

• Random Forests

• Support Vector Machines

• Naive Bayes

• K-Nearest Neighbors

• Gradient Boosting

• Neural Networks
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Q2. Data visualization techniques you have studied and used
Ans. 
I have studied and used various data visualization techniques including bar charts, line graphs, scatter plots, and heat maps.
• I have experience creating bar charts to compare data across categories

• I have used line graphs to show trends over time

• I have created scatter plots to identify correlations between variables

• I have used heat maps to visualize data density and identify patterns
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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. 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. 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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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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