An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The Pre-Master's in Data Science at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| FDA011 | Foundations of Data Science & Algorithmic Problem Solving | Theory | Develop computational thinking, structured problem decomposition and algorithmic problem solving in Data Science. |
| FDA012 | Structured Programming for Data Science with Python | Practical | Write, debug and test clean Python scripts, functions, loops and data structures for Data Science applications. |
| FDA013 | Mathematics & Quantitative Foundations for Data Science | Theory | Solve linear equations, functions, coordinate geometry and basic probability for computing applications in Data Science. |
| FDA014 | Computer Systems Architecture & Terminal Basics in Data Science | Practical | Navigate Linux file systems, manage system processes and write command-line scripts in Bash for Data Science. |
| FDA015 | Academic English & Technical Communication in Data Science | Theory | Develop academic writing, technical reporting, presentation skills and literature research habits in Data Science. |
| FDA016 | Interactive Web Foundations & Digital Interfaces for Data Science | Practical | Create structured web pages using HTML5, CSS3 styling and JavaScript interactivity for Data Science interfaces. |
| FDA017 | Data Representation & Information Structures in Data Science | Theory | Analyze binary representation of numbers, character encodings (ASCII/UTF-8) and data modeling in Data Science. |
| FDA018 | Relational Databases & SQL Essentials Lab for Data Science | Practical | Create relational database tables, insert records and write structured SQL SELECT queries with joins for Data Science. |
| FDA019 | Digital Ethics, Security Awareness & Independent Study in Data Science | Theory | Analyze cyber hygiene principles, intellectual property rules and ethical online academic practices in Data Science. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| FDA021 | Object-Oriented Design & Problem Solving in Data Science | Theory | Model software concepts using object-oriented principles, classes, objects, encapsulation and inheritance in Data Science. |
| FDA022 | Practical Coding Workshop & Lab in Data Science | Practical | Build multi-module programs in Python or Java incorporating modular design patterns and unit tests for Data Science. |
| FDA023 | Discrete Mathematics, Logic & Set Theory Foundations for Data Science | Theory | Apply truth tables, propositional logic statements, sets and basic graph representations to Data Science. |
| FDA024 | Software Engineering Tools, Git & Version Control in Data Science | Practical | Initialize Git repositories, create branches, resolve merge conflicts and collaborate via GitHub in Data Science. |
| FDA025 | Computer Networks, Web Protocols & Internet Systems in Data Science | Theory | Analyze IP addressing, DNS resolution, HTTP request-response cycles and network routers for Data Science. |
| FDA026 | Data Analysis, Spreadsheets & Introductory Pandas Lab for Data Science | Practical | Clean tabular data, compute summary statistics and generate charts using Excel and Python Pandas in Data Science. |
| FDA027 | Operating Systems Fundamentals & System Administration in Data Science | Theory | Explain CPU process scheduling, memory allocation, file systems and operating system security for Data Science. |
| FDA028 | Practical Application Mini-Project in Data Science | Practical | Develop a functional software application or analytical dashboard solving a realistic problem in Data Science. |
| FDA029 | Foundation Capstone Project & Technical Portfolio in Data Science | Practical | Present the completed portfolio of coding artifacts and oral defense of foundational computing concepts in Data Science. |
Secondary school completion or relevant initial technical curiosity. English proficiency recommended.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.