Did you know data science is the most promising career in our digital world. Before entering the data science world you must make sure to learn Python, use functions, work with data structures like lists and dictionaries, and understand OOP.
Specially:
- NumPy, Pandas, Matplotlib/Seaborn
- Pandas
- Matplotlib/Seaborn
- You don’t need to master deep learning or memorize every ML paper. But know what regression, classification, and clustering are. Overfitting, accuracy, precision, AUC, and RMSE, are the essential concepts.
- Start with Scikit-learn like a Swiss Army knife — simple, elegant, and powerful. Once you’re comfortable, move on to PyTorch or TensorFlow if you crave deeper learning.
Don’t chase the shiny stuff — SOTA models, transformers, or 100-layer neural nets — right away. For 90% of Kaggle competitions, the heavy lifting is done through smart data cleaning and insightful feature engineering, not deep magic.
Enter the Newbie Village
Every great journey begins in a beginner zone. Luckily, Kaggle has its version of “Newbie Village,” where you can cut your teeth.
Start with these:
- Titanic — Machine Learning from Disaster:
- Predict survival on the Titanic. Classic binary classification. Small dataset. Clean structure. Great first mission.
- House Prices — Advanced Regression Techniques: Predict housing prices based on various features. You’ll learn how to deal with categorical/numerical data, missing values, and feature engineering.
- Digit Recognizer (MNIST): Dip your toes into image data. Play with basic neural nets if you’re feeling adventurous.
But here’s the catch:
Don’t just upload a CSV and call it a day. Do the whole process — from Exploratory Data Analysis (EDA) to submission. This is how you learn.
- Ask: What does each feature mean?
- Visualize: Are there patterns? Outliers? Missing data?
- Preprocess: Encode, scale, and fill in gaps.
- Model: Start simple. Think logistic regression and decision trees.
- Submit. Evaluate. Iterate.
Then — study the notebooks. Kaggle’s community is its goldmine. Learn from top-scoring solutions. Observe how seasoned players think, code, and structure their projects. It’s not copying — it’s mentorship at scale.
Stand on the Shoulders of Giants
When you’ve graduated from Newbie Village, it’s time to observe the masters.
- Browse active or recently ended competitions.
- Read the winning solutions. Not just for the code — for the ideas. Feature engineering tricks, and cross-validation setups.
- Clear logic
- Beautiful visualizations
- Well-commented code
- Thoughtful experimentation
Feel free to borrow styles, workflows, and even boilerplate setups. Just remember: Understand why they do what they do. Blindly copying is like using someone else’s gym membership — you’ll never actually build your own strength.
Practice Relentlessly
Here’s where the real growth begins.
- Pick a competition that sparks your interest. Ignore the leaderboard if you must.
- Build a simple but complete baseline model. Think of it as your starting point.
- Try new preprocessing techniques.
- Add features.
- Tune hyperparameters.
- Compare models.
Track your scores. Think critically about why things improve or decline. Develop hypotheses, test them, and analyze outcomes.
Kaggle is a Mirror.
Kaggle doesn’t just train your technical skills. It shows you who you are as a learner.
- Are you curious?
- Do you persevere?
- Can you debug your thinking, not just your code?
Treat Kaggle not as a battleground, but as a dojo. Show up. Practice. Learn. Help others. You don’t need a PhD, fancy title, or AI in your job description to call yourself a data scientist. You just need curiosity, humility, and persistence. And Kaggle? It’s the best classroom you never knew you had.
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