SYRKA
person
Degree MatrixModule IntelCareer VectorSkillsDirectivesProfile
Matrix Status: Active
Target: Machine Learning Engineer · 41%

Degree Execution
Matrix

Analyzing active academic modules. Dependency conflicts detected. Prioritization override recommended for optimal career vector alignment.

warning1 Dependency Conflict
Directive: Initialize M249 → Unblock TM258
Stage 1 — Foundation · 120 credits4/4 modules · COMPLETED
MODULE 01completed

TM111 - Introduction to computing and information technology 1

Introductory OU computing module covering digital technologies, data, web technologies, problem solving, graphical programming, networks, IoT and the social context of computing.

+3%toward ML Engineer6.100L
MODULE 02completed

TM112 - Introduction to computing and information technology 2

Extends foundational programming and computing knowledge from TM111. Covers further computing concepts, introductory software development, and problem solving.

+4%toward ML Engineer6.0002
MODULE 03completed

MST124 - Essential mathematics 1

Core mathematical knowledge for technical and AI-related study. Algebra, functions, calculus, mathematical methods for STEM.

+5%toward ML Engineer6.042J
do_not_disturb_onStudent elected MST124 — this module not required
MODULE 04skipped

MU123 - Discovering mathematics

Alternative Stage 1 mathematics option. Introductory mathematics, numeracy, algebra, graphs, problem solving.

+2%toward ML Engineer18.01SC
Stage 2 — Algorithmic Core · 120 credits4/4 active · 1 conflict
MODULE 05ready

TM129 - Technologies in practice

Practical technology and software/system development. Supports progression into advanced computing and AI study.

+7%toward ML Engineer6.005
MODULE 06ready

M249 - Practical modern statistics

Modern statistical techniques for data analysis and AI-related study. Covers probability, statistical inference, regression.

+5%toward ML Engineer18.650
MODULE 07ready

M248 - Analysing data

Applied data-focused mathematics/statistics. Data analysis, mathematical modelling, applied quantitative methods.

+5%toward ML Engineer18.650
blockPrerequisite: M249 Required
MODULE 08blocked

TM258 - Machine learning and artificial intelligence

Core Stage 2 AI module. Machine learning and AI concepts: classification, regression, model building, introductory AI methods.

+12%toward ML Engineer6.036
Stage 3 — Advanced Systems · 120 credits0/4 modules · LOCKED
MODULE 09locked

TM358 - Machine learning and artificial intelligence in practice

Advanced Stage 3 AI module. Applying ML and AI methods in practical, real-world settings.

+18%toward ML Engineer6.034 + 6.S897
MODULE 10locked

TM351 - Data management and analysis

Advanced data management and analysis in complex computing contexts.

+8%toward ML Engineer6.830
MODULE 11locked

TM359 - Systems thinking: managing complexity

Systems thinking, complexity, and management of interconnected problems. Relevant to ethical and real-world AI deployment.

+5%toward ML EngineerRES-15-004
MODULE 12locked

TM470 - The computing and IT project

Capstone. Students complete an independent computing and IT project drawing together knowledge from across the degree.

Priority
04
Risk
MEDIUM
ROI
EXTREME
+15%toward ML Engineer