📘 Data-driven Systems Engineering

University of Trieste

This repository contains materials, notebooks, and exercises for the course Data-driven Systems Engineering, focusing on the intersection of Data Analysis, Software Engineering, and Machine Learning Operations (MLOps).

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📑 Course Description

The course introduces students to data-driven approaches in systems engineering, providing both theoretical foundations and practical implementations in Python. The content is divided into three main sections:

1. Data Analysis using Python

• 1.1 Introduction to Python and its applications in data analysis
• 1.2 Data cleaning and preprocessing techniques
• 1.3 Exploratory Data Analysis (EDA) and Data Visualization
• 1.4 Machine Learning for Data Analysis

2. Software Engineering

• 2.1 Software Evolution: software lifecycle, costs, maintenance, logical design, and real-world models
• 2.2 Methodologies: software development models (Waterfall, Prototyping Cycle, Agile), Agile vs. Traditional approaches, Extreme Programming Guidelines
• 2.3 Unified Modeling Language (UML): visual design approaches, advantages of diagrams, types of UML diagrams

3. Model Learning Operations (MLOps)

• 3.1 MLOps - What and Why: significance and purpose in ML workflows
• 3.2 People in MLOps: roles of data scientists, engineers, and DevOps professionals
• 3.3 MLOps - Features: automation, monitoring, collaboration tools
• 3.4 MLOps - Practice: deployment, version control, CI/CD pipelines
• 3.5 Data Representation, Data Science, and Data Engineering: representation techniques, principles, and fundamentals

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📂 Repository Structure

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├── notebooks/         # Jupyter Notebooks for hands-on exercises
├── data/              # Example datasets used in exercises
├── src/               # Python scripts for reusable functions
├── requirements.txt   # Python dependencies
└── README.md          # Course documentation

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⚙️ Setup Instructions

1. Clone the repository:

   git clone https://github.com/<your-username>/data-driven-systems-engineering.git
   cd data-driven-systems-engineering

2. Create and activate a Python environment (recommended: Python 3.11):

   python3.11 -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\Scripts\activate      # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Launch Jupyter Notebook:

   jupyter notebook

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🎯 Learning Outcomes

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

• Apply data analysis and visualization techniques using Python
• Implement machine learning models for classification and regression
• Understand software engineering methodologies and UML for system design
• Apply MLOps principles to ensure reproducibility, scalability, and maintainability in ML workflows

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👩‍🏫 Target Audience

This course is designed for:

• Students in Engineering and Computer Science
• Professionals interested in Data Science, Software Engineering, and MLOps
• Researchers exploring data-driven approaches to systems engineering

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📌 License

This repository is for educational purposes as part of the University of Trieste’s curriculum.
All rights reserved © University of Trieste.