S&P Global Interview Questions, Process, and Tips for Freshers

Q1. What is Bank reconciliation?
Ans. 
Bank reconciliation is the process of comparing a company's financial records with the bank's records to ensure they match.
• Bank reconciliation helps identify discrepancies such as missing transactions or errors in recording.

• It involves comparing the ending balance on the bank statement with the ending balance in the company's accounting records.

• Adjustments may need to be made for outstanding checks, deposits in transit...read more
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Q1. Capital budgeting & cash flow statement
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Q1. Fundamentals, financial statements
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Q2. Project details, intrests
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Q1. Introduction . project
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Q2. General information , Finance knowledge
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Q1. Tell Me About Your Selfie
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Q2. Explain Internship Expericence
Ans. 
Completed a 6-month data analysis internship at XYZ Company
• Worked on various data analysis projects to extract insights and trends

• Utilized tools such as Excel, SQL, and Tableau to manipulate and visualize data

• Collaborated with team members to present findings and recommendations

• Gained hands-on experience in data cleaning, data visualization, and statistical analysis
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Q1. Financial statements
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Q2. EPS, Cost of Capital
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The questions were related to finance questions and some are from exel related . So read the question carefully and attempt your best.

Q1. The interview was basic fiance questions
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Q1. 1) What Is IPO 2) Sides Of Balance Sheet 3) What Is Depreciation 4) Financial Statements
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Q2. Tell Me About Yourself About Your Family Background & Education
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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.