AI in Data Sciences

Week 1: Database + Machine Learning Foundations

1. Day 01: Database Refresher – RDBMS, SQL, Joins, Indexes, Transactions, and ACID Properties
2. Day 02: Advanced SQL – Window Functions, Common Table Expressions (CTEs), and Stored Procedures for Analytics
3. Day 03: Python for Database Management – psycopg2, SQLAlchemy, Pandas, and SQL Workflows
4. Day 04: Machine Learning Fundamentals – Train-Test Split, Regression, Classification, and Clustering
5. Day 05: Hands-on Lab – Load a Sales Database, Perform Exploratory Data Analysis, and Build ML Predictions Using Python

Week 2: AI-Enhanced Database Operations

1. Day 06: AI for Query Optimization – Learned Indexes and ML-Based Cardinality Estimation
2. Day 07: Anomaly Detection in Databases – Fraud Detection and Log Analysis Using Isolation Forest
3. Day 08: AutoML and Automated Feature Engineering Directly from SQL Tables
4. Day 09: AI for Database Administration – Automated Tuning, Predictive Scaling, and Workload Forecasting
5. Day 10: Mini Project – Build an AI Database Administrator Assistant to Suggest Indexes Using Query Logs

Week 3: Vector Databases + Generative AI

1. Day 11: Introduction to Embeddings – Vector Representations and Cosine Similarity
2. Day 12: Vector Databases – Fundamentals of Pinecone, Weaviate, pgvector, and Milvus
3. Day 13: Retrieval-Augmented Generation (RAG) Architecture – Integrating LLMs, Vector Databases, and SQL Databases
4. Day 14: Text-to-SQL – Using LLMs to Convert Natural Language Questions into SQL Queries
5. Day 15: Hands-on Project – Build a Business Intelligence Chatbot That Answers Questions from a SQL Database Using RAG

Week 4: AI Agents + Data Pipelines + Capstone

1. Day 16: AI Agents for Databases – Building LangChain Agents for Controlled Database Read/Write Operations
2. Day 17: Real-Time AI and Streaming Data – Introduction to Kafka, Flink, and Online Machine Learning
3. Day 18: Data Governance and AI – PII Detection, Data Privacy, and LLM-Based Data Quality Checks
4. Day 19: MLOps for Databases – Model Deployment, Monitoring Data Drift, and Automated Retraining
5. Day 20: Capstone Project and Final Presentation – Choose One: AI Customer Support Agent, Text-to-SQL Dashboard Generator, or Database Transaction Anomaly Detection System