Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence – Telegram
Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
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Free Datasets For Data Science Projects & Portfolio

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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.

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Like if you need similar content 😄👍

Hope this helps you 😊

#ai #datascience
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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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All the best 👍👍
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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.
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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.
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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
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