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.
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.
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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.
💰 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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🚀 The demand for data professionals is skyrocketing in 2025, & 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗮 𝗱𝗲𝗴𝗿𝗲𝗲 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱!🚨
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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.
More data career advice - how to prepare, how to go through interview, how to look for job - you can find here
✔️ 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.
More data career advice - how to prepare, how to go through interview, how to look for job - you can find here
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To automate your daily tasks using ChatGPT, you can follow these steps:
1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.
2. Create ChatGPT Scripts: Use ChatGPT to create noscripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.
3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.
4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.
5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.
6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.
1. Identify Repetitive Tasks: Make a list of tasks that you perform regularly and that can potentially be automated.
2. Create ChatGPT Scripts: Use ChatGPT to create noscripts or workflows for automating these tasks. You can use the API to interact with ChatGPT programmatically.
3. Integrate with Other Tools: Integrate ChatGPT with other tools and services that you use to streamline your workflow. For example, you can connect ChatGPT with task management tools, calendar apps, or communication platforms.
4. Set up Triggers: Set up triggers that will initiate the automated tasks based on certain conditions or events. This could be a specific time of day, a keyword in a message, or any other criteria you define.
5. Test and Iterate: Test your automated workflows to ensure they work as expected. Make adjustments as needed to improve efficiency and accuracy.
6. Monitor Performance: Keep an eye on how well your automated tasks are performing and make adjustments as necessary to optimize their efficiency.
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These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊
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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
Like for more ❤️
ENJOY LEARNING 👍👍
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
Like for more ❤️
ENJOY LEARNING 👍👍
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Forwarded from Data Science & Machine Learning
7 Free APIs for your next Projects