Global Data & AI community 🌍 Learn by doing, work on collaborative projects and solutions, showcase your work, build real experience and grow. Join us 👇
Joined April 2023
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Growth happens when learning moves beyond understanding and becomes something you practise, build, and share.
DaAI Community retweeted
When you're learning a new skill or working on a project, what kind of support would help you make better progress?
#DaAI #DataAndAI #DataAnalytics #AI
100%Project teammates
0%Guidance from experts
0%Feedback on my projects
0%A place to share my work
4 votes • Final resultsHot take: You don't need to know everything to contribute to a project.
You might bring the right question, someone else brings technical skills, and another spots an overlooked problem.
That's the value of building together.
Different strengths. Better perspectives.
Learning a new skill comes with challenges:
• Finding practical projects
• Understanding unfamiliar concepts
• Choosing suitable tools
• Knowing what to learn next
• Staying consistent when progress feels slow
Small, regular practice helps turn uncertainty into progress.
You can understand a concept, follow tutorials, and complete courses without knowing how to apply what you've learned to a real problem.
That gap is where practical projects become valuable.
- For those exploring data science or machine learning, building and evaluating models helps turn theoretical knowledge into practical experience.
- Developing AI applications also gives you opportunities to solve problems with what you've learned.
When you're learning a new skill or working on a project, what kind of support would help you make better progress?
#DaAI #DataAndAI #DataAnalytics #AI
100%Project teammates
0%Guidance from experts
0%Feedback on my projects
0%A place to share my work
4 votes • Final resultsWant to build a Data or AI project but aren't sure where to start?
Choose a clear problem and build something small that you can explain, improve, and showcase.
Here are three ideas and tools you can use 👇
**3. AI-powered text classifier**
Python runs the workflow. scikit-learn can train and evaluate a classifier using labelled examples. Hugging Face offers pretrained models and NLP tools to explore.
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Following a tutorial can help you understand a concept, but what happens when you have to solve a problem yourself?
That’s often where real learning begins. You must decide what to try, test your ideas, and work through problems when your first approach fails.
Collaboration brings different Data and AI skills together.
A data analyst defines the questions. A data engineer prepares the data, while a data scientist or AI engineer explores models and builds solutions.
Everyone contributes, and everyone has something to learn.
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Collaborative projects are about more than finishing.
They help you practice problem-solving, learn from others, and build work you can explain and share.
What part of a real Data or AI project would you most like to tackle with others?
#DaAI #DataAndAI
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