Data Analyst Interview Questions for Infosys

1. What is the role of a Data Analyst?

Answer:
A Data Analyst collects, processes, and performs statistical analysis of data to help companies make informed decisions. The role involves interpreting data, analyzing trends, generating reports, and presenting findings using visualization tools.

2. What are the different types of data you might work with?

Answer:
Data Analysts typically work with:

• Structured Data: Data in a predefined format, like databases (e.g., SQL tables).
• Unstructured Data: Raw data without a specific structure, such as text, social media posts, images.
• Semi-structured Data: Data that has some structure, like JSON or XML files.

3. What is data cleaning, and why is it important?

Answer:
Data cleaning involves removing or correcting inaccuracies, inconsistencies, and missing values in a dataset. It’s essential to ensure that the data is accurate, reliable, and suitable for analysis, as poor data quality can lead to incorrect conclusions.

4. Explain the difference between “inner join” and “outer join.”

Answer:

• Inner Join: Combines rows from both tables where there is a match between the columns. Rows without matches are excluded.
• Outer Join: Returns all rows from both tables, filling in gaps with NULL values where there is no match.

5. What are some common data visualization tools you’ve used?

Answer:
Common tools include:

• Excel: Widely used for simple data analysis and charting.
• Tableau: A powerful visualization tool for creating interactive dashboards.
• Power BI: Microsoft’s data visualization tool with integration for various data sources.
• Google Data Studio: Free tool for creating customizable reports and dashboards.

6. What is the purpose of normalization in databases?

Answer:
Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller ones and defining relationships between them, which helps in efficient data storage and retrieval.

7. Can you explain what a pivot table is and when you would use it?

Answer:
A Pivot Table is a data summarization tool used in Excel (and other tools) that allows you to aggregate, analyze, and compare data. It helps in transforming rows of data into a more understandable, compact summary format for analysis, such as calculating totals, averages, and counts.

8. What is a correlation, and how is it different from causation?

Answer:

• Correlation is a statistical measure that describes the relationship between two variables. A high correlation means that the variables change together.
• Causation indicates that one variable directly affects the other. “Correlation does not imply causation,” meaning just because two variables correlate doesn’t mean one causes the other.

9. How do you handle missing data in a dataset?

Answer:
There are several ways to handle missing data:

• Remove the rows/columns with missing values (if they are not significant).
• Impute missing values using statistical methods like mean, median, or mode.
• Use prediction models (e.g., regression) to estimate missing values.
• Flag the missing data for future investigation or consideration.

10. What is the difference between descriptive, diagnostic, predictive, and prescriptive analytics?

Answer:

• Descriptive Analytics: Focuses on summarizing historical data to understand what has happened.
• Diagnostic Analytics: Investigates data to understand why something happened.
• Predictive Analytics: Uses historical data to predict future outcomes.
• Prescriptive Analytics: Recommends actions to take to achieve desired outcomes based on data analysis.

11. What is SQL, and how is it used in data analysis?

Answer:
SQL (Structured Query Language) is a language used for managing and querying relational databases. Data Analysts use SQL to retrieve, manipulate, and analyze data stored in relational databases by writing queries that filter, aggregate, and join datasets.

12. Can you explain the concept of “outliers” and how to handle them?

Answer:
Outliers are data points that significantly differ from the rest of the dataset. They can distort analysis and results. Handling outliers may involve:

• Removing them if they are errors or irrelevant.
• Transforming the data (e.g., applying logarithmic transformations).
• Capping the outliers to a defined threshold.

13. What are some key statistical concepts that a Data Analyst should know?

Answer:

• Mean, Median, and Mode: Measures of central tendency.
• Standard Deviation and Variance: Measures of data spread.
• Probability Distributions: Understanding normal, binomial, and other distributions.
• Hypothesis Testing: Used to test assumptions.
• Regression Analysis: Modeling relationships between variables.

14. How do you prioritize tasks when working with multiple datasets and deadlines?

Answer:
I prioritize based on the business objectives, data complexity, and deadlines. I typically break down tasks into manageable chunks, focus on the highest-priority tasks first, and ensure constant communication with stakeholders to adjust priorities if necessary.

15. How do you ensure the accuracy and integrity of your analysis?

Answer:
I ensure data accuracy by performing data cleaning, validating assumptions, cross-checking results with peers, and using automated tests. I also document my process thoroughly for transparency and reproducibility.