Name: Advanced AI for Data Analytics: GenAI, Agents & Automation
Rating: 4.3 (44 reviews)

Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Created byStarweaver Experts, Ritesh Vajariya, Paul Siegel

Last updated 4/2026

English

What you'll learn

Design and implement end-to-end data analytics pipelines for real-world business and generative AI applications.
Learn how to use AI for data analytics and apply generative AI for data analytics across practical business scenarios.
Develop and deploy autonomous AI agents and multi-agent systems for advanced AI-powered data exploration.
Understand prompt engineering and LLM chain management to optimize AI data analysis workflows.
Use AI tools for data analytics to improve efficiency, scalability, and data-driven decision-making.
Explore how AI transforms data analytics for businesses, including performance and customer insights.
Build expertise in data analysis in AI using modern AI transformation analytics tools.
Learn how to use AI to analyze data for predictive insights, automation, and real-time analytics.

Course content

23 sections • 133 lectures • 8h 49m total length

Course Introduction3:58
Overview of the course for students to understand what is the course about and instructor introduction
Generative AI Impact on Engineering4:58
Explore how Generative AI is transforming engineering workflows, productivity, and innovation.
Fundamentals of Generative AI Systems Architecture3:08
Detailed breakdown of fundamental components that power modern generative AI systems
Setting Up GenAI Development Environments: Local & Cloud11:48
Step-by-step walkthrough of setting up local and cloud development environments for GenAI
Enterprise Implementation Success Stories3:10
Analysis of successful GenAI implementations across different industry sectors with metrics
Hands-On-Learning: Introduction to Generative AI0:14
A Survey of Generative Artificial Intelligence0:15

LLM Components and Core Mechanics5:22
Deep dive into the foundational elements that make up modern language models
Enterprise LLM Model Comparison3:08
Comprehensive comparison of leading LLM models with their strengths and use cases
LLM Integration and API Setup5:59
Practical demonstration of integrating different LLM APIs into development workflows
Strategic Model Selection Framework3:13
Systematic approach to choosing the right LLM based on project requirements and constraints
Hands-On-Learning: LLM Integration and API Setup0:17
A Brief Survey of Large Language Models0:14

Enterprise GenAI Application Matrix5:29
Structured analysis of GenAI applications across different business functions and departmentsStructured analysis of GenAI applications across different business functions and departments
Industry-Specific Solution Architecture4:01
Detailed examination of GenAI solutions customized for different industry requirements
Support Assistant System Design7:51
Complete walkthrough of designing and planning a customer support AI assistant system
ROI Measurement and Metrics3:57
Comprehensive framework for measuring and tracking GenAI implementation success metrics
Hands-On-Learning: Support Assistant System Design0:16
Generative AI Use Cases: A Primer0:14
GenAI Use Cases

Prompt Engineering Core Principles4:20
Comprehensive introduction to the fundamental concepts and principles of effective prompt design
Structural Elements of Prompts2:59
Detailed analysis of key components that make up well-structured and effective prompts
Interactive Prompt Building Workflow8:39
Hands-on demonstration of building, testing, and iterating different types of prompts
Prompt Design Pattern Library2:47
Exploration of proven prompt patterns and when to apply each for optimal results
Hands-On-Learning: Interactive Prompt Engineering: Systematic Optimization0:12
Prompt Engineering Guide0:13

Multi-Step Prompt Chain Design5:14
Advanced techniques for creating complex, multi-step prompt chains for sophisticated tasks
Context Window Optimization Techniques3:13
Strategies for maximizing context window usage and managing long-form prompt interactions
Advanced Prompt Implementation Guide12:46
Step-by-step implementation of advanced prompting techniques with real-world examples
Error Handling Strategy Framework3:04
Comprehensive approach to handling edge cases and errors in prompt responses
Hands-On-Learning: Advanced Prompt Implementation Guide0:16
A Survey on Prompt Engineering for Large Language Models0:15
Persuading Stakeholders to Invest in a GenAI-Powered Customer Support System

Support Bot Conversation Design5:33
Detailed approach to designing natural and effective customer support conversations
Response Template Architecture Design3:03
Framework for creating flexible and maintainable customer support response templates
Template System Implementation Guide10:54
Complete walkthrough of building and testing a support response template system
Quality Assurance Testing Framework3:09
Comprehensive system for testing and validating support bot responses and interactions
Prompt Engineering for Developers0:13
Hands-On-Learning: Template System Implementation Guide0:16
Prompt Engineering for Developers0:13
Use Case Implementation
Project: GenAI Engineering System Design Challenge0:09

Data Pipeline Requirements Analysis4:31
Comprehensive overview of data requirements and preprocessing needs for GenAI systems
Enterprise Data Pipeline Design4:11
Detailed examination of data pipeline architectures and integration points for RAG
Data Processing System Implementation8:36
Hands-on demonstration of building robust data processing pipelines for GenAI
Data Quality Validation Framework3:57
Complete framework for ensuring and maintaining high-quality data for RAG systems
Hands-On-Learnings: Data Processing0:17
Data Preparation for Machine Learning0:14

RAG System Architecture Design5:20
In-depth exploration of RAG architecture components and their interactions
RAG Architecture: Component Integration Fundamentals4:09
Comprehensive breakdown of essential RAG components and their integration patterns
RAG Implementation Best Practices9:04
Step-by-step guide to implementing a basic RAG system with best practices
RAG System Testing Protocol4:16
Systematic approach to testing and validating RAG system performance and accuracy
Hands-On-Learning: RAG Fundamentals0:17
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks0:13

Advanced RAG Pattern Analysis6:53
Detailed examination of complex RAG patterns and their enterprise applications
Performance Optimization Techniques Framework5:04
Comprehensive strategies for optimizing RAG system performance and response time
Complex RAG System Development13:14
Complete implementation of advanced RAG features and optimization techniques
Enterprise Integration: Best Practices5:00
Best practices for integrating RAG systems with existing enterprise infrastructure
Hands-On-Learning: Advanced RAG: Complex RAG System Development0:18
Retrieval-Augmented Generation: Recent Advances and Future Directions0:14

Support Documentation Processing Framework5:32
Systematic approach to processing and structuring support documentation for RAG
Knowledge Base Architecture Design5:15
Detailed design principles for building effective support knowledge base systems
Support RAG Implementation Guide10:56
Complete walkthrough of building a RAG system for customer support applications
Response Quality Enhancement Strategy5:51
Advanced techniques for improving RAG response accuracy in support scenarios
Hands-On-Learning: RAG for Customer Support: Production-Ready System0:18
Building an Intelligent Customer Support Chatbot with RAG: A Complete Guide0:20
RAG for Customer Support
Project: Enterprise RAG System Design Challenge0:10

Requirements

Learners should have experience with Python programming and a foundational understanding of machine learning and data analysis in AI.
Familiarity with APIs, cloud platforms, and command-line tools will help you work effectively with AI tools for data analytics and implementation labs.
This course is ideal for those looking to apply AI for data analytics, including generative AI for data analytics and real-world AI systems.

Description

Ever wondered how ChatGPT-like systems are built and deployed in real-world environments? Ready to move beyond prompts and learn how to use AI for data analytics, automation, and intelligent systems?

Welcome to Generative AI Engineering - a complete, hands-on program designed to help you build production-ready AI systems while understanding how AI-powered data exploration and analytics tools are transforming modern businesses.

This course goes beyond theory. You’ll learn how to design, build, and deploy scalable AI systems that integrate AI for data analytics, enabling smarter decision-making, automation, and performance optimization.

Throughout the program, you will develop the ability to:

Design scalable architectures for AI-powered data exploration and analytics

Apply generative AI for data analytics to extract insights and automate analysis

Understand how modern AI tools for data analytics improve efficiency, accuracy, and speed

Build systems that showcase how AI can transform data analytics for businesses and brands

Leverage advanced AI transformation analytics tools in production environments

The program is highly application focused. Through hands-on labs and guided projects, you will:

Develop robust data pipelines for AI-driven data analytics systems

Build and deploy AI agents capable of analyzing data and generating actionable insights

Apply prompt engineering techniques to improve AI-generated analytical outputs

Implement RAG (Retrieval-Augmented Generation) for knowledge-driven analytics

Fine-tune models for specialized AI data analysis tasks

Develop multi-agent systems for complex workflows and analytics automation

Deploy production-ready systems with monitoring, safety, and performance optimization

You will also gain experience in:

How to use AI to analyze data and extract meaningful insights

Applying AI for performance analysis in real-world scenarios

Understanding best AI tools for data analysis and how to select the right tools

Implementing AI in data analytics for business and education use cases

By the end of this course, you will be able to design, build, and deploy intelligent systems that combine generative AI engineering with advanced data analytics capabilities.

Don’t just use AI tools but learn how to build them. Transform your skills into high-demand expertise in AI for data analysts, AI engineering, and data analytics transformation.

Who this course is for:

Software engineers, data scientists, and ML engineers looking to build expertise in generative AI for data analytics and AI-powered systems.
Professionals transitioning into AI roles, including those pursuing a data analyst course or complete data analyst bootcamp with AI integration.
Data analysts and engineers who want to learn how to use AI for data analytics, automation, and advanced data exploration.
DevOps and platform engineers managing AI-driven deployments, analytics systems, and scalable AI infrastructure.
Anyone looking to master AI tools for data analytics, build intelligent systems, and understand how AI transforms data analytics across industries.

What you'll learn

Explore related topics

Course content

Introduction to Generative AI7 lectures • 28min

Large Language Models6 lectures • 18min

GenAI Use Cases6 lectures • 22min

Prompt Engineering Basics6 lectures • 19min

Advanced Prompt Engineering7 lectures • 25min

Use Case Implementation8 lectures • 24min

Data Processing6 lectures • 22min

RAG Fundamentals6 lectures • 23min

Advanced RAG6 lectures • 31min

RAG for Customer Support7 lectures • 28min

Requirements

Description

Who this course is for: