ORBIT & SKYLINE INDIA Data Analyst Interview Questions and Answers

Q1. What are joins and its types?
Ans. 
Joins are used to combine rows from two or more 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.

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

• FULL ...read more
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Q2. What are aggregate functions?
Ans. 
Aggregate functions are functions in databases that perform a calculation on a set of values and return a single value.
• Aggregate functions are used to perform operations on a group of rows and return a single result.

• Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.

• For example, SUM function calculates the total of a column, AVG calculates the average, COUNT counts the number of rows, MIN finds the minimu
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Q1. Coding questions in python eda part
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Q1. Where do u see in next 5 years
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Ppt preparation on real estate on deck making skills

Q1. About you previous job
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Q2. About data and stuff , excel and analytics skill and power BI
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Q1. Python question for data cleaning
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Q2. Sql question on data manipulation
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Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
Ans. 
Hyperparameters of SVM include C, kernel, gamma, degree, and coef0.
• C: Regularization parameter that controls the trade-off between achieving a low error on the training data and minimizing model complexity.

• Kernel: Specifies the type of hyperplane used to separate the data.

• Gamma: Kernel coefficient for 'rbf', 'poly', and 'sigmoid' kernels.

• Degree: Degree of the polynomial kernel function.

• Coef0: Independent term in kernel...read more
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Q2. How to tune that hyperparameters
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Hyperparameters can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Grid search: Exhaustively search through a specified subset of hyperparameters.

• Random search: Randomly sample hyperparameter combinations.

• Bayesian optimization: Use probabilistic models to predict the performance of different hyperparameter configurations.
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Prepare for general aptitude test

Q1. Tell me about yourself
Ans. 
I am a data engineer with a strong background in programming and data analysis.
• Experienced in designing and implementing data pipelines

• Proficient in SQL, Python, and big data technologies like Hadoop and Spark

• Skilled in data modeling and optimization techniques

• Worked on projects involving ETL processes and data warehousing
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Q2. Explain the project
Ans. 
Developed a data pipeline to ingest, clean, and analyze customer feedback data for product improvement.
• Created ETL processes to extract data from various sources

• Implemented data cleaning and transformation techniques

• Utilized machine learning algorithms for sentiment analysis

• Generated visualizations and reports for stakeholders
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Q1. CTC discussion based on your skills
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Q2. Work related queries if you have.
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Q1. About previous projects.
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Q1. Salary negotiations.
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