JPMorgan Chase & Co. Data Analyst Interview Questions and Answers

Q1. Get the maximum salary of the employee from the employee and department table
Add your answer
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
Answered by AI
Add your answer
Q3. Explain the challenges you faced in your project
Ans. 
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
Answered by AI
Add your answer

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
Answered by AI
Add your answer
Q2. Query to find the top three salaried employees of each department
Ans. 
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
Answered by AI
Add your answer

English, quant, logical reasoning

Q1. Wacc, coc, beta
Add your answer
Q2. Coc, equity, capm
Add your answer

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
Answered by AI
Add your answer
Q2. Tell the output a**2
Ans. 
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)
Answered by AI
Add your answer
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])
Answered by AI
Add your answer