Engineering Fundamentals
Build the skills you’ll use throughout AI engineering: write and debug Python, work with other developers, understand the mathematics, and prepare data for analysis.
Start with Terminal & Algorithmic Basics. If you’re new to the curriculum, check the entry requirements below first.
Your learning path
Complete these three modules. Each page links to its learning resources, practice tasks, and completion requirements.
Terminal & Algorithmic Basics
Use the terminal, set up Python, collaborate with Git, debug code, and solve algorithmic problems.
Math for AI
Use vectors and matrices in AI computations, reason about probability, and interpret statistical evidence.
Data Foundations
Prepare and query data with NumPy, Pandas, and SQL; explain findings with clear visualizations.
Start with the developer tools in Module 1. Study the introductory NumPy section before the linear algebra notebooks in Math for AI. You can continue algorithm practice alongside the other modules.
Before you start
You need basic Python, foundational algebra, and enough English to follow technical instructions. You do not need calculus or prior experience with data libraries.
Check your entry skills
Basic Python Programming
Before starting the stage, learners should be able to:
- Use variables, common data types and basic operators.
- Write conditional statements and loops.
- Define functions with parameters and return values.
- Work with lists, dictionaries, sets and strings.
- Import and use a Python module.
- Run, modify and explain a short Python program.
Advanced algorithms, object-oriented design and experience with data libraries are not entry requirements.
Foundational Mathematics
Learners should be comfortable with:
- Arithmetic, fractions, decimals and percentages.
- Ratios, powers and square roots.
- Variables and algebraic expressions.
- Rearranging and solving simple linear equations.
- Reading coordinates and simple graphs.
Calculus is not required. Linear algebra, probability and statistics are developed during the stage.
English Reading
You should be able to follow written instructions, understand programming problem statements and use introductory documentation in English. You may use a dictionary to clarify unfamiliar vocabulary.
Readiness Verification
A short Python exercise and foundational mathematics check should confirm readiness. Learners should explain their approach and adapt a solution when given a small change.
Identified gaps should be addressed through preparatory practice before beginning the modules that depend on those skills.
Preparation Within the Stage
Before beginning the linear algebra notebooks, learners must complete introductory NumPy topics covering array creation, shapes, dimensions, indexing and basic array operations.
This preparation is part of Engineering Fundamentals. Terminal skills, Git, debugging, Pandas, SQL and the remaining NumPy topics are also taught within the stage.
A short Python exercise and mathematics check confirm readiness. Address any gaps before starting the modules that depend on them.
Electives
Choose an elective when you want to explore a skill further. Start after completing its prerequisites. Complete exactly two electives to finish Engineering Fundamentals.
Finish this stage
Complete all three required modules and two electives. Keep your code, notebooks, explanations, and assessment results ready for review. Demonstrate the skills on each module page and revise any gaps after feedback.
Statistics requires at least 80% on every assigned unit test and a separate Academy verification. Other modules use their stated practical requirements; there is no overall percentage score.
Next stage: Core Systems.
Plan your study time
Include time for practice, debugging, feedback, and revision. Estimates supplied in the guide are provisional; other workloads remain pending pilot. Agree a schedule with your cohort or mentor.
