TransOrg Analytics Data Analyst Interview Questions, Process, and Tips

IT WAS Difficult and i gave up

Q1. Linked list search binary
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Q2. Array search and traverse
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Q1. Get the maximum salary of the employee from the employee and department table
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Q2. Explain the data analysis process that you would follow for employee salary table
Ans. 
The data analysis process for an employee salary table involves data cleaning, exploration, visualization, and modeling.
• Start by cleaning the data to remove any duplicates, missing values, or outliers.

• Explore the data by calculating basic statistics like mean, median, and standard deviation.

• Visualize the data using histograms, box plots, or scatter plots to identify patterns or trends.

• Perform statistical modeling to an...read more
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Q3. Explain the challenges you faced in your project
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I faced challenges in managing large datasets and ensuring data accuracy in my project.
• Managing large volumes of data from multiple sources

• Ensuring data accuracy and consistency

• Dealing with missing or incomplete data

• Handling data cleaning and preprocessing tasks

• Addressing technical issues with data storage or processing tools
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Q1. Load data from hdfs using python
Ans. 
Use PyArrow library to load data from HDFS in Python
• Install PyArrow library using pip install pyarrow

• Use pyarrow.hdfs.connect to connect to HDFS

• Use pyarrow.parquet.read_table to read data from HDFS
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Q2. Query to find the top three salaried employees of each department
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Query to find the top three salaried employees of each department
• Use a SQL query with a window function like ROW_NUMBER() to rank employees within each department based on salary

• Partition the data by department and order by salary in descending order

• Filter the results to only include rows where the row number is less than or equal to 3
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English, quant, logical reasoning

Q1. Wacc, coc, beta
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Q2. Coc, equity, capm
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Q1. What is p value?
Ans. 
A p-value is a measure used in statistical hypothesis testing to determine the strength of evidence against the null hypothesis.
• A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true.

• A p-value is compared to a significance level (usually 0.05) to determine if the null hypothesis should be rejected.

• A p-value less than the significance level indicates st
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Q2. Tell the output a**2
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The output of a**2 is the square of the value of a.
• The output is the value of a multiplied by itself

• For example, if a = 3, then the output would be 9 (3*3)
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Q3. Difference between append() and concat
Ans. 
append() adds elements to a single DataFrame, while concat() combines multiple DataFrames.
• append() is a method used to add rows to a DataFrame.

• concat() is a function used to combine multiple DataFrames along a particular axis.

• append() modifies the original DataFrame, while concat() returns a new DataFrame.

• Example: df1.append(df2) vs pd.concat([df1, df2])
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