S&P Global Data Analyst Interview Questions, Process, and Tips

Q1. What is reverse merger
Ans. 
Reverse merger is a process by which a private company acquires a public company to bypass the lengthy and complex process of going public.
• A reverse merger involves a private company merging with a public company that is already listed on a stock exchange.

• The private company then becomes a subsidiary of the public company, allowing it to go public without an initial public offering (IPO).

• This process is often used by p...read more
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Q2. Questions related to capital market
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Normal Aptitude Test

Q1. About Myself and Past Experience
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Q2. Question related to Finance and Team working on
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Q1. What do you want to change ?
Ans. 
I want to change the way data is collected and analyzed to improve efficiency and accuracy.
• Implement automated data collection processes to reduce manual errors

• Utilize advanced data analytics tools to uncover insights more effectively

• Improve data visualization techniques for better communication of findings
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Q2. How you can be good fit ?
Ans. 
I have a strong background in data analysis, with experience in various tools and techniques.
• I have a degree in statistics or a related field

• I am proficient in programming languages such as Python and R

• I have experience working with SQL databases and data visualization tools like Tableau

• I have a strong attention to detail and problem-solving skills

• I have experience in interpreting and presenting data to stakeholders
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Sure, here are some example coding questions for a data analyst interview:

1. **Data Cleaning**: Given a dataset with missing values in the "age" column, write a Python function using Pandas to fill in the missing values with the mean age.

2. **Data Manipulation**: Write a SQL query to retrieve the top 10 customers who made the highest number of purchases from a transactions table.

3. **Data Visualization**: Using Matplotlib or Seaborn, create a histogram to visualize the distribution of sales prices from a dataset of real estate transactions.

4. **Statistical Analysis**: Calculate the correlation coefficient between two numerical variables in a dataset using Python and explain what the result indicates about their relationship.

5. **SQL Queries**: Write a SQL query to find the total revenue generated by each product category from a sales database.

6. **Algorithmic Questions**: Write a Python function to find the median of a list of numbers without using any built-in functions for sorting.

These questions cover a range of skills commonly required for data analyst positions, including data cleaning, manipulation, visualization, statistical analysis, SQL querying, and algorithmic problem-solving.

A problem solving aptitude game based test which is also known as pymetric test.

Q1. 1. Basic question about yourself
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Q2. 2. Related to domain
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