Multi-Source Data Extraction & Visualization

3-Day Intensive Course for Analysts, Engineers & Technical Managers
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)

Learning Path Visual

From raw data to clean insights across systems and formats:

Day 1: Data Extraction from IoT Devices & APIs
Understand the landscape of connected devices and streaming data. Learn to extract sensor and telemetry data from IoT platforms using Python, REST APIs, and CSV/JSON integrations.

Day 2: Data Cleaning & Structuring with NumPy & Pandas
Use Python’s core data tools to organize and clean data. Learn best practices for working with arrays, time-series, missing values, multi-source merges, and relational joins.

Day 3: Visualizing Data with Matplotlib & Insights Communication
Transform complex datasets into clear visual stories. Learn to use Matplotlib for custom graphs, dashboards, and real-time monitoring. Communicate findings for reports, teams, or strategic decisions.

Course Overview

This course teaches you how to extract, clean, and visualize data from multiple real-world sources — including IoT systems, CSV/JSON files, and live APIs — using industry-standard Python tools. Whether you’re managing devices, running experiments, or analyzing supply chain data, you’ll learn how to go from raw data to structured insights in three intensive days.

Perfect for those who want to go beyond Excel, and turn sensor streams, logs, and web services into actionable visual intelligence.

You’ll learn how to:

• Connect to and extract data from IoT endpoints, web APIs, and raw files

• Structure and clean datasets using NumPy arrays and Pandas DataFrames

• Merge and process multiple data sources into usable formats

• Visualize trends, distributions, and anomalies with Matplotlib

• Handle time-series and real-time data challenges

• Communicate technical results clearly to decision-makers

What’s Inside Each Day

Day 1 — Data Extraction from IoT Devices & APIs

• IoT data formats and logging practices

• Connecting to REST APIs and local sensors

• Extracting data from JSON, CSV, and streaming endpoints

• Timestamp handling and standardization

• Workshop: Connect to a mock IoT sensor API and extract structured output
  Toolkit: API query templates + data reader scripts
  Focus: Connectivity • Streaming Data • Preprocessing

Day 2 — Data Cleaning & Structuring with NumPy & Pandas

• Array operations and reshaping with NumPy

• DataFrames: indexing, filtering, and aggregation

• Handling missing values, duplicates, and errors

• Combining datasets: joins, merges, group-by logic

• Workshop: Clean and merge three data sources (IoT, CSV, API)
  Toolkit: Pandas recipe sheet + merge troubleshooting guide
  Focus: Data Integrity • Preparation • Multi-Source Logic

Day 3 — Visualizing Data with Matplotlib & Insights Communication

• Plotting fundamentals: line charts, histograms, bar plots

• Multi-variable and time-series plots

• Anomaly detection and event markers

• Styling, labeling, and storytelling with visuals

• Workshop: Create a real-time status dashboard for simulated IoT data
  Toolkit: Graph builder checklist + style library
  Focus: Interpretation • Reporting • Decision Support

Course Goals

By the end of this course, you will be able to:

• Extract and preprocess structured data from diverse digital sources

• Clean and join datasets for analysis using NumPy and Pandas

• Build real-time or batch reports from device, user, or operational data

• Communicate patterns, trends, and anomalies through visual dashboards

• Use Python confidently to integrate IoT, web, and file-based data

Who Should Take This Course?

This course is for IT professionals, managers, and analysts who need to understand and manage cybersecurity risks in a business and compliance context.

Class Reference: HIRE/ENG
Form Updated on: 06/19/2025 (Version 1)
Last Modified on: 06/19/2025

Program Note
Participants will receive downloadable code templates, sensor data simulators, Pandas cheat sheets, API testing tools, and a ready-to-use data visualization toolkit.

Brief pédagogique en français

EXP/10 Multi-Source Data Extraction & Visualization est présenté ici en version synthétique française afin que les équipes pédagogiques puissent évaluer rapidement l'intérêt du module.

Le module s'inscrit dans la famille Analytics et intelligence artificielle. Il peut être adapté au calendrier de l'école, au niveau Tous niveaux, au volume horaire 21 h et aux modalités d'évaluation prévues.

Objectif d'intervention

Ce module vise à relier les outils data, IA et automatisation à des usages professionnels concrets.

Livrables et activités possibles

• cas d'usage, prompts, scénarios d'automatisation ou analyses data
• évaluation critique des résultats, limites et risques
• communication claire des choix techniques et business

Adaptation école

LC peut ajuster le déroulé, la langue d'enseignement, les supports, les exercices et les critères d'évaluation selon la promotion, le diplôme, le niveau d'autonomie attendu et les contraintes de planning.

Pour une version détaillée du syllabus en français, LC confirme le programme final après cadrage du niveau, des heures, du calendrier et des livrables attendus.