Benmalek Zohir

This short talk will introduce the essential steps in data analysis using Python. Participants will learn how to load, clean, and manipulate data, as well as explore techniques for visualizing insights.
What participants will learn:
● Loading data from CSV
● Cleaning and preprocessing data
● Data exploration and visualization using pandas and matplotlib
● Transforming and manipulating data (filtering, grouping, aggregation)
Workshop Program (30 minutes):
Introduction to Data Analysis in Python (5 minutes)
○ The importance of data analysis in Python
○ Key libraries: pandas, matplotlib, numpy

Data Loading & Cleaning (10 minutes)
○ Importing data from CSV
○ Handling missing values and inconsistencies with pandas

Data Exploration (5 minutes)
○ Summarizing data with pandas functions (head(), info(), etc.)
○ Visualizing data distributions using matplotlib.pyplot

Data Manipulation (5 minutes)
○ Filtering and selecting data with conditions
○ Grouping and aggregation with pandas (groupby(), agg())

Q&A (5 minutes)
○ Answering participants' questions

This workshop introduces advanced Python programming concepts through the creation and explanation of Retrieval-Augmented Generation (RAG) systems. Participants will learn the essential components of RAG, implement a system, and understand its applications in modern AI.
What participants will learn:
● Fundamentals of RAG systems and their applications
● Document processing and embedding creation techniques
● Working with vector databases (e.g., FAISS)
● Query processing and retrieval mechanisms
● Prompt engineering for effective RAG implementation
● Integration with Large Language Models (LLMs)
Workshop Program (1h15):
Introduction to RAG Systems (10 minutes)
○ Overview of RAG architecture
○ Applications and benefits

RAG System Schema (10 minutes)
○ Detailed walkthrough of the RAG pipeline
○ Components and their interactions

Document Processing (10 minutes)
○ Techniques for processing various document types
○ Preparing data for embedding

Embedding Creation (10 minutes)
○ Understanding embeddings
○ Implementing embedding models in Python

Vector Database Storage (10 minutes)
○ Introduction to FAISS
○ Storing and indexing embeddings

User Query Processing (5 minutes)
○ Applying the embedding model to user queries

Retrieval Mechanisms (10 minutes)
○ Similarity search techniques
○ Visualizing retrieval process with diagrams

Prompt Engineering (5 minutes)
○ Crafting effective prompts for RAG
○ Best practices and examples

LLM Integration (5 minutes)
○ Connecting RAG output to an LLM
○ Processing and returning responses

Q&A Session (10 minutes)
○ Answering participants' questions