Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

Hello everyone here are some tableau projects along with the datasets to work on

1. Sales Performance Dashboard:
   - Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
   - UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)

2. Customer Segmentation Analysis:
   - Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
   - UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)

3. Inventory Management Dashboard:
   - Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
   - Data.gov: [Inventory datasets](https://www.data.gov/)

4. Financial Analysis Dashboard:
   - Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
   - Quandl: [Financial datasets](https://www.quandl.com/)

5. Social Media Analytics Dashboard:
   - Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
   - Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)

6. Website Analytics Dashboard:
   - Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
   - SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)

7. Supply Chain Analysis Dashboard:
   - Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
   - Data.gov: [Supply chain datasets](https://www.data.gov/)

8. Healthcare Analytics Dashboard:
   - CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
   - HealthData.gov: [Healthcare datasets](https://healthdata.gov/)

9. Employee Performance Dashboard:
   - Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
   - Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)

10. Real-time Dashboard:
    - Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
    - Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.

All the best for your career ❤️

Breaking into Data Science doesn’t need to be complicated.

If you’re just starting out,

Here’s how to simplify your approach:

Avoid:
🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
🚫 Spending months on theoretical concepts without hands-on practice.
🚫 Overloading your resume with keywords instead of impactful projects.
🚫 Believing you need a Ph.D. to break into the field.

Instead:

✅ Start with Python or R—focus on mastering one language first.
✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
✅ Dive into a simple machine learning model (like linear regression) to understand the basics.
✅ Solve real-world problems with open datasets and share them in a portfolio.
✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.

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Hope this helps you 😊

#ai #datascience

61 steps to learn Machine Learning

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

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✅Here are 10 acronyms related to Data Science ✅

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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.