AI-Powered Data Analytics with SQL Server and Agentic AI equips data professionals with the knowledge and practical skills to analyze data using Microsoft SQL Server, automate analytical workflows with Agentic AI, and generate actionable business insights through AI-assisted decision-making.
Duration 10 Days – 70 hrs.
Overview
The AI-Powered Data Analytics with SQL Server and Agentic AI Training Course is a comprehensive, hands-on program designed to equip professionals with the skills to build modern AI-driven analytics solutions that automate data analysis, reporting, visualization, and business intelligence workflows.
Traditional business intelligence often requires manual SQL development, report creation, and dashboard design. This course introduces participants to the next generation of analytics using Agentic AI, Large Language Models (LLMs), Microsoft SQL Server, Python, n8n, Streamlit, and modern visualization libraries to create intelligent analytics assistants capable of understanding natural language, generating SQL queries, analyzing enterprise data, producing executive reports, and automating end-to-end reporting processes.
Participants will learn how to integrate AI models with SQL Server, orchestrate intelligent workflows using n8n, perform advanced analytics with Python and pandas, develop interactive dashboards with Streamlit and Plotly, generate PDF and Excel reports programmatically, and implement governance, security, and responsible AI practices suitable for enterprise environments.
The program concludes with a comprehensive capstone project where participants design, build, and present a fully functional AI-powered analytics platform that addresses real-world business reporting and decision-making scenarios.
Objectives
- Understand modern AI-powered analytics architecture and Agentic AI concepts.
- Explain how Large Language Models (LLMs) enhance business intelligence and analytics.
- Translate natural language business questions into optimized SQL queries using AI.
- Build intelligent AI agents using n8n to automate SQL generation and analytics workflows.
- Connect Python applications to Microsoft SQL Server for enterprise data processing.
- Analyze, clean, and transform business data using pandas.
- Develop interactive dashboards using Streamlit and Plotly.
- Generate automated executive PDF and Excel reports programmatically.
- Design AI-assisted business analytics solutions for sales, finance, operations, inventory, and customer analytics.
- Implement responsible AI, governance, monitoring, validation, and security best practices.
- Build and deploy a complete enterprise AI-powered analytics solution.
Target Audience
- SQL Server Users
- Database Developers
- Database Administrators
- SQL Developers
- Business Analysts
- Data Analysts
- BI Developers
- Data Engineers
- Solution Architects
- AI Application Developers
- Software Engineers
- IT Professionals
- Analytics Consultants
- Digital Transformation Teams
- Technical Managers
- Business Managers interested in AI-assisted analytics
Prerequisites
- Basic computer literacy
- Familiarity with Microsoft Windows
- Basic SQL knowledge
- Understanding of relational database concepts
- Experience using SQL Server Management Studio (SSMS)
- Basic Python programming knowledge
- Familiarity with Microsoft Excel
- Basic programming experience in any language
- Basic understanding of business reporting and analytics
- Interest in Artificial Intelligence and automation
Course Outline
Day 1 – SQL Server Foundations and AI Analytics Introduction
Module 1: Introduction to AI-Powered Analytics
- Evolution of Business Intelligence
- Traditional Analytics vs AI Analytics
- Introduction to Agentic AI
- AI Analytics Architecture
- Enterprise AI Analytics Lifecycle
Module 2: Microsoft SQL Server Fundamentals
- SQL Server Architecture
- Databases
- Tables
- Views
- Relationships
- Primary and Foreign Keys
- Data Types
Module 3: SQL Fundamentals
- SELECT
- WHERE
- ORDER BY
- GROUP BY
- HAVING
- Aggregate Functions
- JOIN Operations
Hands-on Labs
- Explore SQL Server databases
- Write SQL queries
- Analyze database relationships
- Retrieve business information
Day 2 – Advanced SQL for Business Analytics
Module 1: Advanced SQL Techniques
- Subqueries
- Common Table Expressions (CTEs)
- Window Functions
- Ranking Functions
- Running Totals
- Pivoting Data
Module 2: SQL Performance Optimization
- Views
- Stored Procedures
- Indexes
- Query Optimization
- Execution Plans
Hands-on Labs: Develop reports including:
- Sales Performance
- Product Performance
- Customer Analytics
- Inventory Reports
- Financial Summaries
Day 3 – Large Language Models for Data Analytics
Module 1: Understanding LLMs
- Large Language Models
- Popular AI Platforms
- Tokens
- Context Windows
- Prompt Engineering
- Responsible AI
- AI Hallucinations
Module 2: Natural Language to SQL
- Schema-Aware Prompting
- Prompt Templates
- SQL Generation
- SQL Validation
- Human-in-the-Loop Review
Hands-on Labs
- Generate SQL using AI
- Compare AI-generated SQL with manual SQL
- Optimize prompts for better query generation
Day 4 – Building AI SQL Agents with n8n
Module 1: Introduction to n8n
- Workflow Fundamentals
- Nodes
- AI Agent Concepts
- Integrating LLMs
- SQL Server Connectivity
Module 2: Building AI Workflows
- Schema Retrieval
- SQL Generation
- SQL Validation
- Query Execution
- Error Handling
- Logging
Hands-on Labs: Build an AI SQL Agent capable of:
- Receiving natural language requests
- Generating SQL queries
- Executing SQL
- Returning business insights
Day 5 – Python for Enterprise Data Analytics
Module 1: Python Foundations
- Python Basics
- Virtual Environments
- Jupyter Notebooks
- Package Management
Module 2: Data Analytics with pandas
- DataFrames
- Reading SQL Data
- Data Cleaning
- Data Transformation
- Aggregation
- Business Calculations
Module 3: Data Preparation
- Calculated Fields
- Time-Series Preparation
- Data Validation
- Exporting Processed Data
Hands-on Labs
- Connect Python to SQL Server
- Analyze enterprise datasets
- Prepare business KPIs
Day 6 – AI-Powered Business Analytics
Module 1: Business Analytics Applications
- Sales Analytics
- Customer Analytics
- Inventory Analytics
- Financial Analytics
- Purchasing Analytics
- KPI Analysis
Module 2: Advanced Analytics
- Trend Analysis
- Forecasting Concepts
- Cohort Analysis
- Anomaly Detection
- AI-Generated Business Insights
Hands-on Labs: Use AI-generated SQL and Python analytics to solve real-world business scenarios.
Day 7 – Interactive Data Visualization with Plotly
Module 1: Data Visualization Principles
- Choosing Effective Charts
- Dashboard Design
- Visual Storytelling
- KPI Design
Module 2: Plotly Visualization
- Bar Charts
- Line Charts
- Pie Charts
- Scatter Plots
- Heat Maps
- Histograms
- Interactive Dashboards
Hands-on Labs: Create:
- Sales Dashboards
- Customer Dashboards
- Inventory Dashboards
- Executive KPI Dashboards
Day 8 – Dashboard Development with Streamlit
Module 1: Streamlit Fundamentals
- Streamlit Architecture
- Interactive Applications
- User Inputs
- Tables
- Charts
- KPI Cards
Module 2: Building AI Analytics Dashboards
- SQL Server Connectivity
- n8n Integration
- Displaying AI Responses
- Dashboard Design
- User Experience
Hands-on Labs: Develop a complete analytics dashboard featuring:
- Natural Language Queries
- Interactive Filters
- Charts
- KPI Cards
- AI-generated Summaries
Day 9 – AI Reporting, Automation, and Enterprise Deployment
Module 1: AI Reporting
- Executive Summaries
- Business Recommendations
- Data Storytelling
- PDF Report Generation
- Excel Report Generation
Module 2: Enterprise Deployment
- Deploying Streamlit
- Deploying n8n
- Running Local LLMs (Ollama)
- API Management
- Security
- Governance
- Human Validation
- Monitoring
- Automated Report Scheduling
Hands-on Labs: Build automated report generation and distribution workflows using n8n.
Day 10 – Enterprise Capstone Project
Module 1: AI Analytics Platform Development
Participants work in teams to build a complete AI-powered analytics solution incorporating:
- Natural Language Interface
- AI SQL Generation
- SQL Validation
- SQL Execution
- Python Data Processing
- Interactive Dashboards
- Executive Summaries
- PDF Reports
- Excel Reports
- Automated AI Workflows
Module 2: Team Presentation
Each team presents:
- Overall Solution Architecture
- AI Workflow Design
- Prompt Engineering Strategy
- SQL Generation Process
- Analytics Dashboard
- Business Insights
- Executive Reports
- Automation Workflow
- Deployment Recommendations
Technologies Covered
| Category | Technology |
| Database | Microsoft SQL Server |
| Database Tool | SQL Server Management Studio (SSMS) |
| AI Models | OpenAI GPT / Enterprise LLMs |
| AI Agent Orchestration | n8n |
| Programming Language | Python |
| Data Processing | pandas |
| Visualization | Plotly |
| Dashboard Development | Streamlit |
| PDF Reporting | ReportLab |
| Excel Reporting | openpyxl |
