🧠 Module 1: Foundation of Data Science

• What is Data Science?

• Data Science vs. Data Analytics vs. Machine Learning vs. AI

• Real-world applications and career paths

• CRISP-DM or lifecycle of a data science project

📊 Module 2: Data Handling & Preprocessing

• Data types and structures (Structured vs. Unstructured)

• Data Cleaning (missing values, outliers)

• Feature Engineering

• Data Transformation (scaling, encoding)

• Introduction to ETL processes

• Tools: Pandas, NumPy, OpenRefine

🗃️ Module 3: Data Management & SQL

• Introduction to Databases

• SQL for Data Science

• Joins, Subqueries, Aggregations

• NoSQL (basic intro to MongoDB or Firebase)

• Big Data fundamentals (Hadoop, Spark basics)

📈 Module 4: Data Visualization

• Importance of storytelling with data

• Charts and plots (bar, line, scatter, heatmaps, etc.)

• Dashboarding

• Tools: Matplotlib, Seaborn, Plotly, Power BI / Tableau (basic)

🧮 Module 5: Statistics & Probability for Data Science

• Descriptive statistics

• Probability distributions

• Hypothesis testing

• Confidence intervals

• Central Limit Theorem

• Correlation vs. Causation

🤖 Module 6: Machine Learning

• Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, SVM

• Unsupervised Learning: Clustering (K-Means, DBSCAN), PCA

• Model evaluation: Accuracy, Precision, Recall, F1-score, AUC

• Model tuning: Cross-validation, GridSearchCV

• Tools: scikit-learn, XGBoost

🧠 Module 7: Deep Learning & Neural Networks

• Introduction to Neural Networks

• CNNs (image data)

• RNNs / LSTM (sequence data)

• Transfer Learning (using pre-trained models)

• Tools: TensorFlow, Keras, PyTorch

📦 Module 8: Natural Language Processing (NLP)

• Text cleaning and preprocessing (tokenization, stemming, etc.)

• Sentiment analysis

• Text classification

• Transformers & LLMs (like BERT, ChatGPT)

• Tools: NLTK, spaCy, Hugging Face Transformers

🔒 Module 9: Data Ethics, Privacy, and Responsible AI

• Bias and fairness in AI

• Data anonymization and privacy laws (GDPR, etc.)

• Explainable AI (XAI)

• Model interpretability: SHAP, LIME

🌐 Module 10: Real-World Projects + MLOps

• Version control (Git, GitHub)

• Cloud deployment (basic: Streamlit, Flask + Heroku/AWS)

• Model monitoring and updates

• CI/CD for ML

• Tools: Docker, MLflow, FastAPI (optional)

📁 Bonus: Capstone Project

• Choose real-world datasets (Kaggle, UCI, etc.)

• End-to-end project (EDA → Modeling → Deployment)

• Present with dashboards or apps

🌟 Trending Topics (Optional Add-ons)

• Generative AI (ChatGPT, DALL·E, etc.)

• AutoML

• TinyML (ML for IoT devices)

• Time Series Forecasting (Prophet, ARIMA)

• Recommender Systems

• Prompt Engineering