Module 1: Foundations of AI and Data Science
- Overview of data science: data collection, cleaning, analysis, visualization
- Introduction to AI: machine learning, deep learning, natural language processing
- Relationship between AI and data science: predictive modeling, automation, insights
Module 2: Data Preparation and Feature Engineering
- Data preprocessing: handling missing values, normalization, encoding
- Feature selection and dimensionality reduction (PCA, LDA)
- Automated feature engineering with AI tools
- Case studies: improving model accuracy through feature engineering
Module 3: Machine Learning in Data Science
- Supervised learning: regression, classification
- Unsupervised learning: clustering, anomaly detection
- Reinforcement learning for decision-making
- Model evaluation: accuracy, precision, recall, F1-score, ROC curves
Module 4: Deep Learning Applications
- Neural networks: CNNs, RNNs, transformers
- AI for image recognition, NLP, and time-series forecasting
- Transfer learning and pre-trained models
- Case studies: healthcare imaging, sentiment analysis, fraud detection
Module 5: AI-Driven Data Analytics
- Predictive analytics for business intelligence
- AI in big data: Hadoop, Spark, cloud-based ML platforms
- Real-time analytics with streaming data
- Visualization powered by AI (automated dashboards, storytelling with data)
Module 6: Tools and Platforms
- Python libraries: TensorFlow, PyTorch, Scikit-learn, Pandas
- AI-powered data science platforms: Azure ML, Google Vertex AI, AWS SageMaker
- AutoML systems for rapid prototyping
- Hands-on labs: building models and deploying them
Module 7: Challenges and Risks
- Data bias and fairness in AI models
- Overfitting and underfitting in machine learning
- Explainability and interpretability of AI-driven insights
- Security and privacy in data science workflows
Module 8: Ethics and Governance
- Responsible AI in data science
- Regulatory frameworks: GDPR, CCPA, NDPR
- Transparency in AI-driven decision-making
- Building trust with stakeholders
Module 9: Future Trends
- Generative AI for synthetic data creation
- AI in automated data storytelling
- AI agents for autonomous data analysis
- Integration of AI with quantum computing for advanced analytics