2024 Diploma Portfolio Requirements

Space Technology Diploma: Curriculum Outline and Portfolio Requirements

Python Programming Student Portfolio Requirements

A notable requirement of the GDST Space Technology Diploma is each individual student's Python programming portfolio, evidencing practical application of units studied during the course of the year. 

Students receiving a full diploma are required to:

• Submit a full portfolio of programming exercises 

• Participate fully in the conference practical activity Viva Challenge at the end of year Student Conference

Students wishing to receive a full diploma with distinction are additionally required to:

• Complete the NASA Earth Science Data Systems extension task utilising their advanced GIOVANNI tool

Students wishing to participate in the WUSAT Programme Satellite Orientation roundtable conference discussion are additionally required to:

• Compete the WUSAT post-lecture extended research activity 

The programming portfolio document aims to double as an interview tool, presenting skills developed during the scope of the programme, and containing samples of tutorial codes, visualisations and interpretive explanations. This year's prescribed portfolio tasks cover the following project exercise briefs:

• Opening Reflection Summary on the Python Programming Skills for Space Technology

• Module 1: James Webb MIRI Instrument Simulation 

• Module 2: NASA Earth Data Analysis & Regression Task

• Module 3: Computer Vision Task

• Module 4: Spatial Movement Classification Task

• Module 5: Aerospace Design Optimisation Task

• Practical Computing Sensor Workshop Task (Raspberry Pi Camera & SenseHAT task)

Curriculum: Programming Modules Description

The foundations of our programme curriculum encourage computer science-specific skills to be more purposefully honed for the space industry and ultimately, to target abilities desired for industry-level internship experiences. 

Module 1: Deep Space Payload Instrument Telemetry, Data Representation and Visualisation 

Simulated payload operations and automated OOP logging and regeneration of 2D array image data 

Module 2: Earth Observation Data Visualisation and Prediction 

Data analysis and machine learning prediction models for accessible NASA Earth Data

Module 3: Computer Vision AI and Image Recognition 

Predictive analysis and machine learning classification models for image data

Module 4: Spatial Movement Sensing and Analysis

Physical sensor data collection and machine learning classification

Module 5: Design Optimisation Algorithms for Airborne Flight

Introduction to airborne optimisation techniques for powering, weight and force constraints