Data-Driven Systems Engineering

The course 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: Understanding the software lifecycle, costs, and maintenance. Exploring logical design and its relation to real-world models.
2.2. Methodologies: Examining software development models such as the Waterfall Model, Prototyping Cycle, and Agile Methodologies. Comparing Agile vs. Traditional Methodologies and Understanding Extreme Programming Guidelines.
2.3. Unified Modeling Language (UML): Introduction to UML for defining visual design approaches. Understanding the advantages of diagrams and exploring various types of UML diagrams.

3 Model Learning Operations (MLOps)
3.1. MLOps – What and Why: An overview of MLOps, explaining its significance and purpose in machine learning operations.
3.2. People in MLOps: Understanding the roles and responsibilities of individuals involved in MLOps, including data scientists, engineers, and DevOps professionals.
3.3. MLOps – Features: Exploring key features and components of MLOps, such as automation, monitoring, and collaboration tools.
3.4. MLOps – Practice: Practical implementation of MLOps principles, including model deployment, version control, and continuous integration/continuous deployment (CI/CD) pipelines.
3.5. Data Representation, Data Science, and Data Engineering: Introduction to data representation techniques, principles of data science, and fundamentals of data engineering.

Course Code: 440MI

Study Path: Department of Engineering and Architecture. Engineering. Computer engineering.

Lecturer: Barbon Junior Sylvio

ECTS: 6

Basic Course (BC): knowledge in area of expertise

Maximum number of T4EU students: No limits

Duration: 23 Sep 2024 – 20 Dec 2024

Time: Monday 1 pm – 4 pm, Tuesday 2 pm – 4 pm and Friday 11 am – 2 pm (CET)