Top Data Analytics Interview Questions (With Sample Answers) ππΌ
1οΈβ£ What is Data Analytics?
Answer: Data Analytics is the process of examining raw data to find patterns, draw conclusions, and make data-driven decisions. It involves steps like data collection, cleaning, analysis, and visualization.
2οΈβ£ What tools are commonly used in Data Analytics?
Answer: Excel, SQL, Python, R, Tableau, Power BI, and Google Analytics are widely used tools. Each serves different purposesβfrom data cleaning to visualization.
3οΈβ£ What is the difference between data cleaning and data transformation?
Answer:
- Data Cleaning: Fixing or removing incorrect, duplicate, or incomplete data.
- Data Transformation: Changing data format or structure (e.g., converting text to numbers, pivoting tables).
4οΈβ£ Explain the difference between Structured and Unstructured data.
Answer:
- Structured: Organized in tables (e.g., SQL databases).
- Unstructured: Free-form data (e.g., emails, images, videos, PDFs).
5οΈβ£ What is the role of SQL in data analytics?
Answer: SQL is used to query databases, retrieve, filter, group, and manipulate data efficiently. It's essential for handling large datasets stored in relational databases.
6οΈβ£ How do you handle missing data?
Answer: Options include:
- Removing rows/columns
- Replacing with mean/median/mode
- Using predictive models for imputation
7οΈβ£ What is data visualization and why is it important?
Answer: It means presenting data in visual formats (charts, graphs) to make insights clear and actionable. Helps non-technical stakeholders understand complex data easily.
8οΈβ£ What are KPIs and how do you choose them?
Answer: Key Performance Indicators are measurable metrics that show how well a process or business is performing. Chosen based on goals and what truly reflects success (e.g., conversion rate, churn rate).
9οΈβ£ Difference between INNER JOIN and LEFT JOIN in SQL?
Answer:
- INNER JOIN: Returns only matching records from both tables.
- LEFT JOIN: Returns all records from the left table + matched from the right.
π Whatβs your experience with real-world data challenges?
Answer: Mention specific examples like dealing with messy datasets, data quality issues, or creating dashboards under time pressure.
π¬ Tap β€οΈ more!
Top comments (0)