Data Analysis & AI for Systems Engineering

1. Training Introduction

The Data Analysis & AI for Systems Engineering program provides systems engineers with the modern analytical, computational, and AI-driven capabilities required to design, optimize, validate, and manage complex engineering systems.

As engineering systems become increasingly interconnected, data-rich, and digitally managed, engineers must integrate data analytics, machine learning, AI-driven optimization, and model-based systems engineering (MBSE) into the full lifecycle—from concept development to operation and maintenance.

This training introduces participants to essential data methods, predictive modelling, AI-enabled system analysis, digital twins, system reliability modelling, autonomous decision support, and cyber-physical system intelligence.

 

2. Training Objective

The program aims to enable participants to:

• Apply data analytics and AI tools to model, assess, and optimize engineering systems.
• Understand statistical, computational, and algorithmic foundations of engineering analytics.
• Integrate ML/AI models into system architecture, design, and operations.
• Develop predictive, prescriptive, and diagnostic models for system performance.
• Utilize digital twins and simulation methods to support decision-making.
• Ensure responsible AI, model reliability, and safe deployment in engineered systems.
• Strengthen professional competence as a modern systems engineer with data and AI capabilities.

 

3. Targeted Group

This programme is designed for:

• Systems engineers & systems architects
• Mechanical, electrical, aerospace, industrial, and civil engineers
• Data analysts working in engineering environments
• MBSE practitioners and digital engineers
• Reliability, operations, and maintenance engineers
• AI engineers transitioning into engineering systems
• Technical managers overseeing digital transformation

 

4. Course Duration

15–16 Days (Standard Programme)

12–13 Days (Accelerated Executive Version)

 

5. Training Methodology

• Interactive instructor-led sessions
• Hands-on coding and modelling using Python, MATLAB, or similar tools
• Data labs for analytics, machine learning, and engineering simulations
• Case studies from aerospace, energy, transportation, manufacturing, and telecom
• Group workshops using MBSE & SysML tools
• System simulation and digital twin labs
• Capstone project integrating AI with systems engineering processes
• Evaluations through quizzes, models, and project demonstrations

 

6. Course Content

Module 1: Introduction to Engineering Data & AI

• Role of data and AI in systems engineering
• Engineering data types and formats
• Overview of AI applications in complex systems

Module 2: Foundations of Systems Engineering & MBSE

• System lifecycle and architecture frameworks
• Requirements, interfaces, and system decomposition
• MBSE workflows and SysML overview

Module 3: Statistical Analysis for Systems Engineers

• Descriptive & inferential statistics
• Probability models
• Hypothesis testing for engineering systems

Module 4: Data Acquisition, Cleaning & Feature Engineering

• Sensor data, time-series, logs, and engineering datasets
• Data preprocessing and transformation
• Handling missing or noisy engineering data

Module 5: Machine Learning for Engineering Applications

• Regression, classification, clustering for engineering
• Model evaluation & selection
• Practical engineering ML exercises

Module 6: AI-Enhanced System Modelling

• AI-supported systems modelling
• Integrating ML models into MBSE workflows
• Hybrid modelling (physics + data)

Module 7: Simulation & Digital Twin Technologies

• System simulation frameworks
• Real-time data integration
• Creating digital twins for monitoring and prediction

Module 8: Optimization Techniques for Engineering Systems

• Linear, nonlinear, and multi-objective optimization
• Heuristics, evolutionary algorithms
• AI-driven optimization case study

Module 9: Reliability, Safety & Risk Modelling

• Reliability engineering metrics (MTBF, hazard rate, RPN)
• Predictive maintenance models
• AI for fault detection and diagnostics

Module 10: Data-Driven Control Systems & Automation

• System control fundamentals
• AI-assisted control strategies
• Adaptive and intelligent control

Module 11: Data Visualization & Decision Support

• Engineering dashboards
• Data storytelling for technical decision-makers
• Visual analytics for complex systems

Module 12: Big Data & Cloud Technologies for Engineering

• Big data frameworks (Hadoop, Spark)
• Cloud analytics platforms (AWS, Azure, GCP)
• Engineering data pipeline architecture

Module 13: AI for Cyber-Physical & Autonomous Systems

• AI techniques for robotics, drones, and autonomous vehicles
• Cyber-physical integration
• Systems intelligence for autonomous operations

Module 14: Cybersecurity for AI-Enabled Systems

• Threat modelling for engineering systems
• Securing data-driven models and pipelines
• Safety, robustness, and adversarial considerations

Module 15: Capstone Project – AI in Systems Engineering

• Develop a complete engineering solution integrating:
• MBSE
• Data analytics
• Machine learning
• Simulation and optimization
• Presentation and technical defence of results

Module 16: Future Trends & Industry Applications of AI in Systems Engineering

• AI for sustainable engineering
• Smart infrastructure, IoT systems, and Industry 4.0
• Emerging tools in engineering intelligence

 

7. Expected Learning Outcomes

Participants will be able to:

• Apply data science and AI techniques to engineering system problems
• Build predictive, diagnostic, and prescriptive models
• Develop engineering simulations and digital twins
• Use AI for system optimization, control, and reliability enhancement
• Integrate AI models with MBSE workflows and system architectures
• Evaluate and deploy safe, ethical, and reliable AI-enabled systems
• Lead or support digital transformation projects in engineering organizations

 

8. Certificate of Completion

Upon completing all modules, hands-on labs, and the capstone project, participants will be awarded:

Certificate of Completion

Data Analysis & AI for Systems Engineering

Issued by FOTADE Training, Research and Resource Development Centre

The certificate confirms that the participant has attained advanced competence in data-driven engineering, AI modelling, and modern systems engineering methodologies for complex system environments.