Agentic Data Analysis with Power BI and Langchain

Agentic Data Analysis with Power BI and Langchain : Content

Power BI Architecture

The course Data Analysis with Power BI starts with an explanation of the architecture of Power BI with Power BI Desktop, Power BI Gateway and Power BI Services and how to create reports with Power BI.

Power BI and Data

Subsequently attention is paid to making connections with data sources such as plain text files, CSV files, SQL Databases, XML data, JSON data and Excel files. It is also discusses how data in the cloud and in online services can be accessed by Power BI.

Power BI Components

The various components that make up Power BI such as Dashboards, Tiles, Power Query, Power Pivot, Power View and Power Map are also treated in the course Data Analysis with Power BI. And the Data Analysis Expressions (DAX) with conditionals, data types and information functions, logical functions and table functions that Power BI has available are reviewed as well.

Data Modeling

Part of the program of the course Data Analysis with Power BI is also Data Modeling in Power BI with relationship detection, calculated columns and tables and DAX formulas and expressions. And attention is paid to the creation and configuration of Dashboards for displaying data.

Filters

Next the various filters that Power BI has to offer are discussed such as Page Level Filters, Report Level Filters and Drill Through Filters and Queries and Slicers in Power BI are treated.

Power BI REST API

Finally the course Data Analysis with Power BI explains and demonstrates the Power BI REST API which provides service endpoints for embedding, administration and user resources.

Intro Agentic AI

The course Agentic AI with LangChain begins with a comprehensive introduction to agentic AI systems, exploring how they differ from traditional chatbots and what makes an agent truly autonomous. The architecture patterns, core components, and the role of LLMs as reasoning engines are discussed, along with common challenges and real-world use cases.

LangChain Fundamentals

This module provides a thorough foundation in the LangChain framework, covering its architecture, the distinction between chains and agents, and essential components like prompt templates, memory modules, and document loaders.

Building First Agent

Here participants create their first functional AI agent from scratch. The module covers choosing appropriate LLMs, defining clear agent goals, writing effective prompts, integrating tools, managing state, and implementing robust error handling.

Agent Tools and Actions

This part focuses on expanding agent capabilities through tools and actions. Participants learn to create custom tools, integrate APIs, connect to databases, implement search functionality, enable web scraping, and handle tool execution errors properly.

Memory and Context

Memory management is explored in depth, covering different memory types including short-term, long-term, conversation buffers, and vector stores. The module addresses entity memory, knowledge graphs, and techniques for optimizing memory.

Multi-Agent Systems

This module introduces collaborative multi-agent systems using frameworks like LangGraph. Topics include agent collaboration patterns, message passing between agents, task decomposition, workflow orchestration, and evaluating multi-agent performance.

RAG and Knowledge

Retrieval Augmented Generation is covered comprehensively, including document processing, embeddings, vector databases, and semantic search. Participants learn chunking strategies, and methods for evaluating RAG system performance.

Production Deployment

Deployment considerations are addressed with attention to API development, scalability, performance optimization, caching, rate limiting, and security best practices. The module also covers monitoring, observability, cost management, and testing strategies.