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How to Find the Right Datasets

Most people search “SQL dataset” and get overused, small samples. Instead, try:

✅ “raw data” – Unstructured, real-world data.
✅ “large dataset” – 100MB+, ideal for indexing and performance tuning.
✅ “financial transactions” – Good for fraud detection projects.
✅ “customer behavior” – Perfect for segmentation analysis.
✅ “time-series” – Best for forecasting trends.

Use filters to find datasets with over 1M rows for real SQL challenges.

Best Kaggle Datasets for SQL Projects

💰 PaySim Fraud Transactions – Detect financial fraud.
🚖 NYC Taxi Trip Data – 1.2B rows, great for query optimization.
📦 Walmart Sales Forecasting – Time-series analysis for sales trends.
🎵 Spotify Music Data – Find hidden patterns in music trends.

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Pro Tips for Portfolio Projects

✔️ Pick a dataset you actually find interesting—you’ll be more engaged.
✔️ Work with messy data—handling nulls, duplicates, and inconsistencies shows real SQL skills.
✔️ Use Kaggle Kernels—learn from real SQL queries and improve your approach.
✔️ Upload your work to GitHub—employers check for structured, well-documented projects.

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Python Programming Interview Questions for Entry Level Data Analyst

1. What is Python, and why is it popular in data analysis?

2. Differentiate between Python 2 and Python 3.

3. Explain the importance of libraries like NumPy and Pandas in data analysis.

4. How do you read and write data from/to files using Python?

5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.

6. What are list comprehensions, and how do you use them in Python?

7. Explain the concept of object-oriented programming (OOP) in Python.


8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.

9. How do you handle missing or NaN values in a DataFrame using Pandas?

10. Explain the difference between loc and iloc in Pandas DataFrame indexing.

11. Discuss the purpose and usage of lambda functions in Python.

12. What are Python decorators, and how do they work?

13. How do you handle categorical data in Python using the Pandas library?

14. Explain the concept of data normalization and its importance in data preprocessing.

15. Discuss the role of regular expressions (regex) in data cleaning with Python.

16. What are Python virtual environments, and why are they useful?

17. How do you handle outliers in a dataset using Python?

18. Explain the usage of the map and filter functions in Python.

19. Discuss the concept of recursion in Python programming.

20. How do you perform data analysis and visualization using Jupyter Notebooks?

Python Interview Q&A: https://topmate.io/coding/898340

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