✅ Data Science Learning Checklist 🧠🔬

📚 Foundations
⦁ What is Data Science & its workflow
⦁ Python/R programming basics
⦁ Statistics & Probability fundamentals
⦁ Data wrangling and cleaning

📊 Data Manipulation & Analysis
⦁ NumPy & Pandas
⦁ Handling missing data & outliers
⦁ Data aggregation & grouping
⦁ Exploratory Data Analysis (EDA)

📈 Data Visualization
⦁ Matplotlib & Seaborn basics
⦁ Interactive viz with Plotly or Tableau
⦁ Dashboard creation
⦁ Storytelling with data

🤖 Machine Learning
⦁ Supervised vs Unsupervised learning
⦁ Regression & classification algorithms
⦁ Model evaluation & validation (cross-validation, metrics)
⦁ Feature engineering & selection

⚙️ Advanced Topics
⦁ Natural Language Processing (NLP) basics
⦁ Time Series analysis
⦁ Deep Learning fundamentals
⦁ Model deployment basics

🛠️ Tools & Platforms
⦁ Jupyter Notebook / Google Colab
⦁ scikit-learn, TensorFlow, PyTorch
⦁ SQL for data querying
⦁ Git & GitHub

📁 Projects to Build
⦁ Customer Segmentation
⦁ Sales Forecasting
⦁ Sentiment Analysis
⦁ Fraud Detection

💡 Practice Platforms:
⦁ Kaggle
⦁ DataCamp
⦁ Datasimplifier

💬 Tap ❤️ for more!