Data Science & Visualization for AI

Module 1: Data Science Fundamentals

• Data collection, cleaning, wrangling (Pandas, Dask)
• Exploratory Data Analysis (EDA) and feature engineering
• Handling big data (Spark, Databricks, Snowflake)

Module 2: Modeling & Machine Learning

• Regression, classification, clustering techniques
• Ensemble methods, time series analysis, anomaly detection
• Model selection, hyperparameter tuning (Scikit-learn, XGBoost, LightGBM)

Module 3: Visualization Tools & Techniques

• Data visualization best practices: Matplotlib, Seaborn, Plotly, Altair
• Interactive dashboards (Tableau, Power BI, Dash)
• Visualizing ML workflow, interpretability (SHAP, LIME)

Module 4: AI for Business Insights

• Translating analysis to business impact
• Automated reporting and storytelling (Jupyter, Google Data Studio)
• Use cases: Financial forecasting, healthcare analytics, marketing performance

Module 5: End-to-End Project & Real Deployment

• Build, validate, and present full data science pipeline
• Productionizing models: MLflow, Airflow, Docker
• Documentation and ethical impact review