🔥 A-Z Data Science Road Map 🧠💡

1. Math and Statistics 📊
- Descriptive statistics
- Probability
- Distributions
- Hypothesis testing
- Correlation
- Regression basics

2. Python Basics 🐍
- Variables
- Data types
- Loops
- Conditionals
- Functions
- Modules

3. Core Python for Data Science 🐼
- NumPy
- Pandas
- DataFrames
- Missing values
- Merging
- GroupBy
- Visualization

4. Data Visualization 🎨
- Matplotlib
- Seaborn
- Plotly
- Histograms, boxplots, heatmaps
- Dashboards

5. Data Wrangling 🧹
- Cleaning
- Outlier detection
- Feature engineering
- Encoding
- Scaling

6. Exploratory Data Analysis (EDA) 🔍
- Univariate analysis
- Bivariate analysis
- Stats summary
- Correlation analysis

7. SQL for Data Science 🗄️
- SELECT
- WHERE
- GROUP BY
- JOINS
- CTEs
- Window functions

8. Machine Learning Basics 🤖
- Supervised vs unsupervised
- Train test split
- Cross validation
- Metrics

9. Supervised Learning ✅
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- Gradient boosting
- SVM
- KNN

10. Unsupervised Learning 🗺️
- K-Means
- Hierarchical clustering
- PCA
- Dimensionality reduction

11. Model Evaluation 📈
- Accuracy
- Precision
- Recall
- F1
- ROC AUC
- MSE, RMSE, MAE

12. Feature Engineering ✨
- One hot encoding
- Binning
- Scaling
- Interaction terms

13. Time Series ⏳
- Trends
- Seasonality
- ARIMA
- Prophet
- Forecasting steps

14. Deep Learning Basics 🧠
- Neural networks
- Activation functions
- Loss functions
- Backprop basics

15. Deep Learning Libraries 🌐
- TensorFlow
- Keras
- PyTorch

16. NLP 💬
- Tokenization
- Stemming
- Lemmatization
- TF-IDF
- Word embeddings

17. Big Data Tools 🐘
- Hadoop
- Spark
- PySpark

18. Data Engineering Basics 🛠️
- ETL
- Pipelines
- Scheduling
- Cloud concepts

19. Cloud Platforms ☁️
- AWS (S3, Lambda, SageMaker)
- GCP (BigQuery)
- Azure ML

20. MLOps ⚙️
- Model deployment
- CI/CD
- Monitoring
- Docker
- APIs (FastAPI, Flask)

21. Dashboards 📊
- Power BI
- Tableau
- Streamlit

22. Real-World Projects 🚀
- Classification
- Regression
- Time series
- NLP
- Recommendation systems

23. Version Control 🔄
- Git
- GitHub
- Branching
- Pull requests

24. Soft Skills 🗣️
- Problem framing
- Business communication
- Storytelling

25. Interview Prep 🧑‍💻
- SQL practice
- Python challenges
- ML theory
- Case studies

📚 Good Resources To Learn Data Science 💡

1. Documentation
- Pandas docs: pandas.pydata.org
- NumPy docs: numpy.org
- Scikit-learn docs: scikit-learn.org
- PyTorch: pytorch.org

2. Free Learning Channels
- FreeCodeCamp: youtube.com/c/FreeCodeCamp
- Data School: youtube.com/dataschool
- Krish Naik: YouTube
- StatQuest: YouTube

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