B.Sc. Mathematics (Honours)

Riverside College of Science · 2025-26 · benchmarked against 8 peer programmes

Peers: Eastbrook Institute of Science, Hillcrest University, Kingsport Science College, Lakeshore University, Meadowvale University, Northfield University, Southbank Institute of Technology, Westgate College

69%Peer benchmark alignment

6Priority gaps (3 high)

6Distinctive strengths

2Course pairs flagged for overlap

7 of 9Requirements met: ugc-ccfup-2022

Not yet analysedIndustry alignment

Priority gaps

Skills that at least half of the peer programmes cover and this one does not. High = 70% or more of peers.

Skill	Finding	Peers	Priority
Optimisation Applied Mathematics	Optimisation is covered by 100% of benchmarked programmes (8 of 8: in the core of 8, only as an elective in 0) but is not found in this curriculum. 8 of them have a course on it (all core). Where peers cover it Eastbrook Institute of Science (a core course on it: Linear Programming): Linear Programming, Multivariable Calculus Hillcrest University (a core course on it: Linear Programming): Introduction to Machine Learning, Linear Programming, Multivariable Calculus Kingsport Science College (a core course on it: Linear Programming): Linear Programming, Multivariable Calculus Lakeshore University (a core course on it: Linear Programming): Linear Programming Meadowvale University (a core course on it: Linear Programming): Linear Programming Northfield University (a core course on it: Linear Programming): Introduction to Machine Learning, Linear Programming, Multivariable Calculus Southbank Institute of Technology (a core course on it: Linear Programming): Introduction to Machine Learning, Linear Programming, Multivariable Calculus Westgate College (a core course on it: Linear Programming): Linear Programming, Multivariable Calculus	100% core 100% core course 100%	High
Python Computational Skills	Python is covered by 75% of benchmarked programmes (6 of 8: in the core of 6, only as an elective in 0) but is not found in this curriculum. 6 of them have a course on it (all core). Where peers cover it Eastbrook Institute of Science (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Numerical Methods Lab (Python), Programming with Python, Scientific Computing Hillcrest University (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Numerical Methods Lab (Python), Programming with Python Kingsport Science College (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python Northfield University (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python Southbank Institute of Technology (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python, Scientific Computing Westgate College (a core course on it: Programming with Python, Numerical Methods Lab (Python)): Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python	75% core 75% core course 75%	High
Data Visualisation Data Skills	Data Visualisation is covered by 75% of benchmarked programmes (6 of 8: in the core of 6, only as an elective in 0) but is not found in this curriculum. None of them has a course on it: they teach it inside other courses. Where peers cover it Eastbrook Institute of Science: Numerical Methods Lab (Python), Programming with Python, Scientific Computing Hillcrest University: Numerical Methods Lab (Python), Programming with Python Kingsport Science College: Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python Northfield University: Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python Southbank Institute of Technology: Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python, Scientific Computing Westgate College: Data Analysis with Python, Numerical Methods Lab (Python), Programming with Python, Spreadsheet Modelling, Statistical Computing with R	75% core 75% core course 0%	High
Internship / Field Work Research & Professional	Internship / Field Work is covered by 50% of benchmarked programmes (4 of 8: in the core of 4, only as an elective in 0) but is not found in this curriculum. 4 of them have a course on it (all core). Where peers cover it Kingsport Science College (a core course on it: Internship): Internship Northfield University (a core course on it: Internship): Internship Southbank Institute of Technology (a core course on it: Internship): Internship Westgate College (a core course on it: Internship): Internship	50% core 50% core course 50%	Medium
Data Analysis Data Skills	Data Analysis is covered by 50% of benchmarked programmes (4 of 8: in the core of 0, only as an elective in 4) but is not found in this curriculum. 4 of them have a course on it (all elective). Where peers cover it Kingsport Science College (elective only; an elective course on it: Data Analysis with Python): Data Analysis with Python Northfield University (elective only; an elective course on it: Data Analysis with Python): Data Analysis with Python Southbank Institute of Technology (elective only; an elective course on it: Data Analysis with Python, Databases for Data Science): Data Analysis with Python, Databases for Data Science Westgate College (elective only; an elective course on it: Data Analysis with Python): Data Analysis with Python	50% core 0% core course 0%	Medium
Mathematical Modelling Applied Mathematics	Mathematical Modelling is covered by 50% of benchmarked programmes (4 of 8: in the core of 0, only as an elective in 4) but is mentioned in only one syllabus line here. 4 of them have a course on it (all elective). The one line found here Differential Equations (RMA204, topic): “Mathematical modelling with differential equations” Where peers cover it Kingsport Science College (elective only; an elective course on it: Mathematical Modelling): Mathematical Modelling Northfield University (elective only; an elective course on it: Mathematical Modelling): Mathematical Modelling Southbank Institute of Technology (elective only; an elective course on it: Mathematical Modelling): Mathematical Modelling Westgate College (elective only; an elective course on it: Mathematical Modelling, Spreadsheet Modelling): Mathematical Modelling, Spreadsheet Modelling	50% core 0% core course 0%	Medium

Watch list

Covered by 30–49% of peers and not here: not yet a norm, but worth discussing.

Skill	Here	Peers covering	Peers mentioning once
Machine Learning & AI Where peers cover it Hillcrest University (elective only; an elective course on it: Introduction to Machine Learning): Introduction to Machine Learning Northfield University (elective only; an elective course on it: Introduction to Machine Learning): Introduction to Machine Learning Southbank Institute of Technology (elective only; an elective course on it: Introduction to Machine Learning): Introduction to Machine Learning	· Not found	38% core 0% core course 0%	0%

Distinctive strengths

Covered here, and fewer than 30% of peers even mention it.

Skill	Here	Peers covering	Peers mentioning once
Mechanics Evidence here Mechanics (RMA401, title): “Mechanics” Mechanics (RMA401, topic): “Statics: equilibrium of forces” Mechanics (RMA401, topic): “Kinematics in one and two dimensions” Mechanics (RMA401, topic): “Projectile motion” Mechanics (RMA401, topic): “Central forces” Mechanics (RMA401, topic): “Simple harmonic motion”	● A core course on it	25% core 25% core course 25%	0%
Metric Spaces Evidence here Metric Spaces (RMA601, title): “Metric Spaces” Metric Spaces (RMA601, topic): “Metric spaces, open and closed sets” Metric Spaces (RMA601, topic): “Complete metric spaces” Metric Spaces (RMA601, topic): “Contraction mapping, Banach fixed point theorem” Metric Spaces (RMA601, topic): “Compactness in metric spaces”	● A core course on it	25% core 25% core course 25%	0%
Vector Calculus Evidence here Calculus II (RMA102, topic): “Gradient, divergence and curl” Calculus II (RMA102, topic): “Line integrals and surface integrals” Calculus II (RMA102, topic): “Green's theorem in the plane” Vector Calculus (RMA103, title): “Vector Calculus” Vector Calculus (RMA103, topic): “Vector differentiation” Vector Calculus (RMA103, topic): “Gradient, divergence and curl” Vector Calculus (RMA103, topic): “Line integrals, surface integrals and volume integrals” Vector Calculus (RMA103, topic): “Green's theorem in the plane” Vector Calculus (RMA103, topic): “Stokes's theorem and Gauss divergence theorem”	● A core course on it	25% core 25% core course 25%	0%
Integral Transforms & Fourier Analysis Evidence here Integral Transforms (RMA502, title): “Integral Transforms” Integral Transforms (RMA502, topic): “Laplace transform and inverse Laplace transform” Integral Transforms (RMA502, topic): “Convolution theorem” Integral Transforms (RMA502, topic): “Fourier series” Integral Transforms (RMA502, topic): “Fourier transform”	○ Covered only by electives (an elective course on it)	25% core 0% core course 0%	0%
C / C++ programming Evidence here Numerical Methods Lab (C) (RMA403, topic): “Programming in C: bisection and Newton-Raphson” Programming in C (RMA404, title): “Programming in C” Programming in C (RMA404, topic): “C programming: data types and control structures” Programming in C (RMA404, topic): “Pointers and structures”	● A core course on it	12% core 12% core course 12%	12%
History of Mathematics & Indian Knowledge Systems Evidence here History of Mathematics (RMA602, title): “History of Mathematics” History of Mathematics (RMA602, topic): “Indian mathematics: Aryabhata, Brahmagupta and Bhaskara” History of Mathematics (RMA602, topic): “The Kerala school” History of Mathematics (RMA602, topic): “Greek mathematics” History of Mathematics (RMA602, topic): “Development of calculus”	○ Covered only by electives (an elective course on it)	12% core 0% core course 0%	0%

Course overlap

Pairs of courses in this programme whose topic lists share much of their vocabulary (cosine similarity of weighted terms, 0–100%). A committee can merge, differentiate or redistribute them. Theory–lab pairs are expected to overlap.

Courses	Similarity	Level	Strongest shared terms
Probability and Statistics RMA303 Statistical Methods RMA501	79%	High	central tendency, chi square, t test, probability, binomial, correlation Shared skills: Correlation & Regression, Descriptive Statistics, Probability, Statistical Inference
Calculus II RMA102 Vector Calculus RMA103	51%	Moderate	line integral, divergence, curl, gradient, green, plane Shared skills: Vector Calculus

Alignment by area

Of the skills that at least half of the peers cover (“peer-core” skills), how many this programme covers. A skill mentioned only once here counts half.

Area	Alignment	Peer-core skills
Pure Mathematics	100%	5
Applied Mathematics	70%	5
Computational Skills	50%	2
Probability & Statistics	100%	2
Data Skills	0%	2
Research & Professional	50%	2

Programme structure

Everything listed for each programme, including every elective alternative, so these are not the credits a student earns. A dash means some courses have no credits recorded.

Programme	Courses offered	Credits offered
Riverside College of Science (this programme)	23	90
Eastbrook Institute of Science	23	92
Hillcrest University	26	102
Kingsport Science College	22	86
Lakeshore University	21	84
Meadowvale University	20	76
Northfield University	23	92
Southbank Institute of Technology	21	84
Westgate College	20	76
Peer median	21.5	85

Coverage matrix

● a core course on it · ✓ covered (a course title, or two or more syllabus lines) · ○ covered, but only by elective courses · △ mentioned in one line · · not found.

Skill	This programme	Eastbrook Institute of Science	Hillcrest University	Kingsport Science College	Lakeshore University	Meadowvale University	Northfield University	Southbank Institute of Technology	Westgate College	Peers
Pure Mathematics
Calculus	●	●	●	●	●	●	●	●	●	100%
Real Analysis	●	●	●	●	●	●	●	●	●	100%
Linear Algebra	●	●	●	●	●	●	●	●	●	100%
Abstract Algebra	●	●	●	●	●	●	●	·	●	88%
Complex Analysis	●	●	●	●	●	●	●	·	●	88%
Topology	✓	●	●	·	·	·	●	·	·	38%
Metric Spaces	●	●	●	·	·	·	·	·	·	25%
Number Theory	○	·	○	·	○	○	·	·	·	38%
Discrete Mathematics & Combinatorics	·	·	·	·	●	·	·	●	·	25%
Graph Theory	○	·	○	·	·	·	○	○	·	38%
Vector Calculus	●	●	●	·	·	·	·	·	·	25%
Differential Geometry	·	○	·	·	○	·	·	·	·	25%
Functional Analysis	·	○	·	·	·	·	·	·	·	12%
Applied Mathematics
Ordinary Differential Equations	●	●	●	●	●	●	●	●	●	100%
Partial Differential Equations	●	●	●	●	●	●	●	·	·	75%
Mechanics	●	·	·	·	●	●	·	·	·	25%
Mathematical Modelling	△	·	·	○	·	·	○	○	○	50%
Optimisation	·	●	●	●	●	●	●	●	●	100%
Operations Research	·	·	·	·	○	·	·	○	·	25%
Integral Transforms & Fourier Analysis	○	·	·	·	○	○	·	·	·	25%
Numerical Methods	●	●	●	●	●	●	●	●	●	100%
Financial Mathematics	·	·	·	○	·	·	·	·	○	25%
Cryptography	·	·	○	·	·	·	·	·	·	12%
Computational Skills
Python	·	●	●	●	·	·	●	●	●	75%
R	·	·	·	·	·	·	·	·	○	12%
MATLAB / Octave / Scilab	·	·	·	·	·	·	·	·	·	0%
C / C++ programming	●	·	·	·	●	△	·	·	·	12%
Computer Algebra Systems	·	·	·	·	·	·	·	·	·	0%
Programming Fundamentals	○	●	●	●	△	·	●	●	●	75%
Scientific Computing & Simulation	·	○	·	·	·	·	·	○	·	25%
LaTeX	·	·	○	○	·	·	·	·	·	25%
Probability & Statistics
Descriptive Statistics	●	·	·	△	✓	✓	△	△	✓	38%
Probability	●	●	●	●	△	●	●	●	●	88%
Statistical Inference	✓	●	●	●	△	△	●	●	○	75%
Correlation & Regression	✓	·	△	·	△	△	△	△	○	12%
Sampling & Surveys	·	·	·	·	·	·	·	·	·	0%
Design of Experiments	·	·	·	·	·	·	·	·	·	0%
Time Series & Forecasting	·	·	○	·	·	·	·	·	·	12%
Stochastic Processes	·	·	·	○	·	·	·	·	·	12%
Data Skills
Data Analysis	·	·	·	○	·	·	○	○	○	50%
Data Visualisation	·	✓	✓	✓	·	·	✓	✓	✓	75%
Machine Learning & AI	·	·	○	·	·	·	○	○	·	38%
Databases & SQL	·	·	·	·	·	·	·	○	·	12%
Spreadsheets	·	·	·	·	·	·	·	·	○	12%
Research & Professional
Research Project / Dissertation	●	●	●	●	●	●	●	●	●	100%
Internship / Field Work	·	·	·	●	·	·	●	●	●	50%
Mathematical Communication	·	·	△	○	·	○	△	△	△	25%
History of Mathematics & Indian Knowledge Systems	○	·	·	·	·	○	·	·	·	12%

Regulatory checklist: UGC Curriculum and Credit Framework for Undergraduate Programmes (2022)

Checked as a 3-year UG degree (the programme's degree is recorded as "3-year"). Source: https://www.ugc.gov.in/pdfnews/7193743_FYUGP.pdf.

Required (9): 7 met, 2 partly met.

Requirement	This programme	Expected	Status
A 3-year UG degree needs 120 credits; a 4-year degree (Honours, or Honours with Research) needs 160. p. 10	120 credits	at least 120 credits	Met
A single-major degree needs at least 50% of its credits from the major discipline. p. 10 UGC's examples put this as 60 of 120 credits (3-year) and 80 of 160 (4-year); a programme with more credits than the minimum can meet those figures yet fall under 50%.	55% (66 of 120 credits)	at least 50% of credits in Major (Core)	Met
Table 2: Major (Core), minimum 60 credits (3-year) or 80 (4-year). p. 19	66 credits in Major (Core) Evidence Major, semester I: 8 Major, semester II: 8 Major, semester III: 8 Major, semester IV: 12 Major, semester V: 14 Major, semester VI: 16	at least 60 credits	Met
Table 2: Minor stream, minimum 24 credits (3-year) or 32 (4-year). p. 19	24 credits in Minor stream Evidence Minor, semester I: 4 Minor, semester II: 4 Minor, semester III: 4 Minor, semester IV: 4 Minor, semester V: 4 Minor, semester VI: 4	at least 24 credits	Met
Table 2: Multidisciplinary, minimum 9 credits. p. 19	9 credits in Multidisciplinary Evidence Multidisciplinary, semester I: 3 Multidisciplinary, semester II: 3 Multidisciplinary, semester III: 3	at least 9 credits	Met
Table 2: Ability Enhancement Courses (AEC), minimum 8 credits. p. 20	8 credits in Ability enhancement courses (AEC) Evidence AEC, semester I: 2 AEC, semester II: 2 AEC, semester III: 2 AEC, semester IV: 2	at least 8 credits	Met
Table 2: Skill Enhancement Courses (SEC), minimum 9 credits. p. 20	6 credits in Skill enhancement courses (SEC) Evidence SEC, semester I: 3 SEC, semester III: 3	at least 9 credits	Partly met
Table 2: Value Added Courses common for all UG, 6-8 credits. p. 20	5 credits in Value-added courses (VAC) Evidence VAC, semester II: 3 VAC, semester IV: 2	at least 6 credits (6-8 in the framework)	Partly met
Table 2: Summer Internship, 2-4 credits. p. 20	2 credits in Summer internship Evidence Internship, semester V: 2	at least 2 credits (2-4 in the framework)	Met

Course outcomes

Each outcome's level in the revised Bloom's taxonomy, read from the verbs that open it (1 Remember, 2 Understand, 3 Apply, 4 Analyse, 5 Evaluate, 6 Create). Higher-order means Analyse, Evaluate or Create. Outcomes that open with a verb hard to assess, such as understand or learn, are marked vague.

77 outcomes in 23 of 23 courses · 20% higher-order · 16 vague (21%) · 0 with no action verb · 1 stated for more than one course.

Year	Outcomes	Levels	Higher-order	Vague
Year 1	32	Remember 8 · Understand 8 · Apply 16	0%	8
Year 2	24	Understand 8 · Apply 8 · 8 no level	0%	8
Year 3	21	Apply 7 · Analyse 7 · Evaluate 7	67%	0

16 vague outcomes: understand (8), learn (8)

RMA101: Understand the basic ideas of Calculus
RMA102: Understand the basic ideas of Calculus II
RMA103: Understand the basic ideas of Vector Calculus
RMA104: Understand the basic ideas of Real Analysis I
RMA201: Understand the basic ideas of Linear Algebra
RMA202: Understand the basic ideas of Group Theory
RMA203: Understand the basic ideas of Real Analysis II
RMA204: Understand the basic ideas of Differential Equations
RMA301: Learn the main techniques of Ring Theory
RMA302: Learn the main techniques of Complex Analysis
RMA303: Learn the main techniques of Probability and Statistics
RMA304: Learn the main techniques of Partial Differential Equations
RMA401: Learn the main techniques of Mechanics
RMA402: Learn the main techniques of Numerical Methods
RMA403: Learn the main techniques of Numerical Methods Lab (C)
RMA404: Learn the main techniques of Programming in C

1 outcome is stated for more than one course

RMA101, RMA102, RMA103, RMA104, RMA201, RMA202, RMA203, RMA204: Use mathematical software to check their answers

Every outcome, by course

Calculus RMA101 · semester I

2Understand the basic ideas of Calculus (vague)
1State the main definitions and results of Calculus
3Solve standard problems in Calculus
3Use mathematical software to check their answers

Calculus II RMA102 · semester I

2Understand the basic ideas of Calculus II (vague)
1State the main definitions and results of Calculus II
3Solve standard problems in Calculus II
3Use mathematical software to check their answers

Vector Calculus RMA103 · semester I

2Understand the basic ideas of Vector Calculus (vague)
1State the main definitions and results of Vector Calculus
3Solve standard problems in Vector Calculus
3Use mathematical software to check their answers

Real Analysis I RMA104 · semester I

2Understand the basic ideas of Real Analysis I (vague)
1State the main definitions and results of Real Analysis I
3Solve standard problems in Real Analysis I
3Use mathematical software to check their answers

Linear Algebra RMA201 · semester II

2Understand the basic ideas of Linear Algebra (vague)
1State the main definitions and results of Linear Algebra
3Solve standard problems in Linear Algebra
3Use mathematical software to check their answers

Group Theory RMA202 · semester II

2Understand the basic ideas of Group Theory (vague)
1State the main definitions and results of Group Theory
3Solve standard problems in Group Theory
3Use mathematical software to check their answers

Real Analysis II RMA203 · semester II

2Understand the basic ideas of Real Analysis II (vague)
1State the main definitions and results of Real Analysis II
3Solve standard problems in Real Analysis II
3Use mathematical software to check their answers

Differential Equations RMA204 · semester II

2Understand the basic ideas of Differential Equations (vague)
1State the main definitions and results of Differential Equations
3Solve standard problems in Differential Equations
3Use mathematical software to check their answers

Ring Theory RMA301 · semester III

2Explain the methods of Ring Theory
3Apply Ring Theory to problems from science and economics
–Learn the main techniques of Ring Theory (vague)

Complex Analysis RMA302 · semester III

2Explain the methods of Complex Analysis
3Apply Complex Analysis to problems from science and economics
–Learn the main techniques of Complex Analysis (vague)

Probability and Statistics RMA303 · semester III

2Explain the methods of Probability and Statistics
3Apply Probability and Statistics to problems from science and economics
–Learn the main techniques of Probability and Statistics (vague)

Partial Differential Equations RMA304 · semester III

2Explain the methods of Partial Differential Equations
3Apply Partial Differential Equations to problems from science and economics
–Learn the main techniques of Partial Differential Equations (vague)

Mechanics RMA401 · semester IV

2Explain the methods of Mechanics
3Apply Mechanics to problems from science and economics
–Learn the main techniques of Mechanics (vague)

Numerical Methods RMA402 · semester IV

2Explain the methods of Numerical Methods
3Apply Numerical Methods to problems from science and economics
–Learn the main techniques of Numerical Methods (vague)

Numerical Methods Lab (C) RMA403 · semester IV

2Explain the methods of Numerical Methods Lab (C)
3Apply Numerical Methods Lab (C) to problems from science and economics
–Learn the main techniques of Numerical Methods Lab (C) (vague)

Programming in C RMA404 · semester IV

2Explain the methods of Programming in C
3Apply Programming in C to problems from science and economics
–Learn the main techniques of Programming in C (vague)

Statistical Methods RMA501 · semester V

5Prove the main theorems of Statistical Methods
3Compute with the methods of Statistical Methods in applied problems
4Analyse problems that combine Statistical Methods with earlier courses

Integral Transforms RMA502 · semester V

5Prove the main theorems of Integral Transforms
3Compute with the methods of Integral Transforms in applied problems
4Analyse problems that combine Integral Transforms with earlier courses

Number Theory RMA503 · semester V

5Prove the main theorems of Number Theory
3Compute with the methods of Number Theory in applied problems
4Analyse problems that combine Number Theory with earlier courses

Graph Theory RMA504 · semester V

5Prove the main theorems of Graph Theory
3Compute with the methods of Graph Theory in applied problems
4Analyse problems that combine Graph Theory with earlier courses

Metric Spaces RMA601 · semester VI

5Prove the main theorems of Metric Spaces
3Compute with the methods of Metric Spaces in applied problems
4Analyse problems that combine Metric Spaces with earlier courses

History of Mathematics RMA602 · semester VI

5Prove the main theorems of History of Mathematics
3Compute with the methods of History of Mathematics in applied problems
4Analyse problems that combine History of Mathematics with earlier courses

Project / Dissertation RMA603 · semester VI

5Prove the main theorems of Project / Dissertation
3Compute with the methods of Project / Dissertation in applied problems
4Analyse problems that combine Project / Dissertation with earlier courses

CO–PO mapping: UGC LOCF Mathematics graduate attributes

Which programme outcomes each course outcome serves, on the 1–3 scale of NAAC and NBA matrices (1 slight, 2 moderate, 3 substantial). An outcome serves a PO when its verbs, the skills it names or its words match the PO's indicators; it maps more strongly when indicators of two kinds match, and when its Bloom level reaches the level the PO calls for. A course's cell is the rounded mean over its outcomes that serve the PO. Source: UGC Learning Outcomes-based Curriculum Framework, B.A./B.Sc. (Honours) Mathematics, section 3.

GA7 Self-directed learning, GA8 Moral and ethical awareness/reasoning, GA9 Lifelong learning are served by no course outcome.

PO	Outcome	Courses	Mean
GA1	Disciplinary knowledge Comprehensive knowledge of mathematics and understanding of the disciplines in the programme. (p. 5)	21	2.0
GA2	Communication skills Communicate mathematical concepts effectively, with examples and geometric visualisation, and use mathematics as a precise language. (p. 5)	8	2.0
GA3	Critical thinking and analytical reasoning Think critically about mathematical concepts, analyse results and apply them across branches of mathematics. (p. 5)	7	2.0
GA4	Problem solving Solve problems, including models of growth and decay, linear systems and linear programming, and give new solutions using mathematics. (p. 5)	23	2.9
GA5	Research-related skills Ask appropriate questions about mathematical concepts and follow advances in the subject. (p. 6)	1	3.0
GA6	Information/digital literacy Use appropriate software and programming to solve equations and carry out mathematical investigations. (p. 6)	9	2.0
GA7	Self-directed learning Work independently and study notions of mathematics in depth. (p. 6)	0	–
GA8	Moral and ethical awareness/reasoning Recognise unethical behaviour such as fabricating or misrepresenting data, and act objectively and truthfully. (p. 6)	0	–
GA9	Lifelong learning Acquire knowledge and skills through logical reasoning, and keep learning. (p. 6)	0	–

Course articulation matrix

Course	GA1	GA2	GA3	GA4	GA5	GA6	GA7	GA8	GA9
RMA101 Calculus	2	·	·	3	·	2	·	·	·
RMA102 Calculus II	2	·	·	3	·	2	·	·	·
RMA103 Vector Calculus	2	·	·	3	·	2	·	·	·
RMA104 Real Analysis I	2	·	·	3	·	2	·	·	·
RMA201 Linear Algebra	2	·	·	3	·	2	·	·	·
RMA202 Group Theory	2	·	·	3	·	2	·	·	·
RMA203 Real Analysis II	2	·	·	3	·	2	·	·	·
RMA204 Differential Equations	2	·	·	2	·	2	·	·	·
RMA301 Ring Theory	2	2	·	3	·	·	·	·	·
RMA302 Complex Analysis	2	2	·	3	·	·	·	·	·
RMA303 Probability and Statistics	2	2	·	3	·	·	·	·	·
RMA304 Partial Differential Equations	2	2	·	3	·	·	·	·	·
RMA401 Mechanics	2	2	·	3	·	·	·	·	·
RMA402 Numerical Methods	2	2	·	2	·	·	·	·	·
RMA403 Numerical Methods Lab (C)	2	2	·	2	·	·	·	·	·
RMA404 Programming in C	2	2	·	3	·	2	·	·	·
RMA501 Statistical Methods	1	·	2	3	·	·	·	·	·
RMA502 Integral Transforms	2	·	2	3	·	·	·	·	·
RMA503 Number Theory	2	·	2	3	·	·	·	·	·
RMA504 Graph Theory	2	·	2	3	·	·	·	·	·
RMA601 Metric Spaces	2	·	2	3	·	·	·	·	·
RMA602 History of Mathematics	·	·	2	3	·	·	·	·	·
RMA603 Project / Dissertation	·	·	2	3	3	·	·	·	·
Courses serving	21	8	7	23	1	9	0	0	0

Why each outcome maps where it does

Calculus RMA101

Understand the basic ideas of Calculus
GA1 2 (skill: Calculus)
State the main definitions and results of Calculus
GA1 2 (verb: state, skill: Calculus)
Solve standard problems in Calculus
GA1 2 (skill: Calculus); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Calculus); GA4 2 (verb: use); GA6 2 (words: software)

Calculus II RMA102

Understand the basic ideas of Calculus II
GA1 2 (skill: Calculus)
State the main definitions and results of Calculus II
GA1 2 (verb: state, skill: Calculus)
Solve standard problems in Calculus II
GA1 2 (skill: Calculus); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Calculus, Vector Calculus); GA4 2 (verb: use); GA6 2 (words: software)

Vector Calculus RMA103

Understand the basic ideas of Vector Calculus
GA1 2 (skill: Vector Calculus)
State the main definitions and results of Vector Calculus
GA1 2 (verb: state, skill: Vector Calculus)
Solve standard problems in Vector Calculus
GA1 2 (skill: Vector Calculus); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Vector Calculus); GA4 2 (verb: use); GA6 2 (words: software)

Real Analysis I RMA104

Understand the basic ideas of Real Analysis I
GA1 2 (skill: Real Analysis)
State the main definitions and results of Real Analysis I
GA1 2 (verb: state, skill: Real Analysis)
Solve standard problems in Real Analysis I
GA1 2 (skill: Real Analysis); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Real Analysis); GA4 2 (verb: use); GA6 2 (words: software)

Linear Algebra RMA201

Understand the basic ideas of Linear Algebra
GA1 2 (skill: Linear Algebra)
State the main definitions and results of Linear Algebra
GA1 2 (verb: state, skill: Linear Algebra)
Solve standard problems in Linear Algebra
GA1 2 (skill: Linear Algebra); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Linear Algebra); GA4 2 (verb: use); GA6 2 (words: software)

Group Theory RMA202

Understand the basic ideas of Group Theory
GA1 2 (skill: Abstract Algebra)
State the main definitions and results of Group Theory
GA1 2 (verb: state, skill: Abstract Algebra)
Solve standard problems in Group Theory
GA1 2 (skill: Abstract Algebra); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Abstract Algebra); GA4 2 (verb: use); GA6 2 (words: software)

Real Analysis II RMA203

Understand the basic ideas of Real Analysis II
GA1 2 (skill: Real Analysis)
State the main definitions and results of Real Analysis II
GA1 2 (verb: state, skill: Real Analysis)
Solve standard problems in Real Analysis II
GA1 2 (skill: Real Analysis); GA4 3 (verb: solve, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Calculus, Real Analysis); GA4 2 (verb: use); GA6 2 (words: software)

Differential Equations RMA204

Understand the basic ideas of Differential Equations
GA1 2 (skill: Ordinary Differential Equations); GA4 1 (skill: Ordinary Differential Equations)
State the main definitions and results of Differential Equations
GA1 2 (verb: state, skill: Ordinary Differential Equations); GA4 1 (skill: Ordinary Differential Equations)
Solve standard problems in Differential Equations
GA1 2 (skill: Ordinary Differential Equations); GA4 3 (verb: solve, skill: Ordinary Differential Equations, words: problems)
Use mathematical software to check their answers
GA1 1 (course: Mathematical Modelling, Ordinary Differential Equations); GA4 2 (verb: use); GA6 2 (words: software)

Ring Theory RMA301

Explain the methods of Ring Theory
GA1 3 (verb: explain, skill: Abstract Algebra); GA2 2 (verb: explain)
Apply Ring Theory to problems from science and economics
GA1 2 (skill: Abstract Algebra); GA4 3 (verb: apply, words: problems)
Learn the main techniques of Ring Theory
GA1 1 (skill: Abstract Algebra)

Complex Analysis RMA302

Explain the methods of Complex Analysis
GA1 3 (verb: explain, skill: Complex Analysis); GA2 2 (verb: explain)
Apply Complex Analysis to problems from science and economics
GA1 2 (skill: Complex Analysis); GA4 3 (verb: apply, words: problems)
Learn the main techniques of Complex Analysis
GA1 1 (skill: Complex Analysis)

Probability and Statistics RMA303

Explain the methods of Probability and Statistics
GA1 3 (verb: explain, skill: Descriptive Statistics, Probability); GA2 2 (verb: explain)
Apply Probability and Statistics to problems from science and economics
GA1 2 (skill: Descriptive Statistics, Probability); GA4 3 (verb: apply, words: problems)
Learn the main techniques of Probability and Statistics
GA1 1 (skill: Descriptive Statistics, Probability)

Partial Differential Equations RMA304

Explain the methods of Partial Differential Equations
GA1 3 (verb: explain, skill: Partial Differential Equations); GA2 2 (verb: explain)
Apply Partial Differential Equations to problems from science and economics
GA1 2 (skill: Partial Differential Equations); GA4 3 (verb: apply, words: problems)
Learn the main techniques of Partial Differential Equations
GA1 1 (skill: Partial Differential Equations)

Mechanics RMA401

Explain the methods of Mechanics
GA1 3 (verb: explain, skill: Mechanics); GA2 2 (verb: explain)
Apply Mechanics to problems from science and economics
GA1 2 (skill: Mechanics); GA4 3 (verb: apply, words: problems)
Learn the main techniques of Mechanics
GA1 1 (skill: Mechanics)

Numerical Methods RMA402

Explain the methods of Numerical Methods
GA1 3 (verb: explain, skill: Numerical Methods); GA2 2 (verb: explain); GA4 1 (skill: Numerical Methods)
Apply Numerical Methods to problems from science and economics
GA1 2 (skill: Numerical Methods); GA4 3 (verb: apply, skill: Numerical Methods, words: problems)
Learn the main techniques of Numerical Methods
GA1 1 (skill: Numerical Methods); GA4 1 (skill: Numerical Methods)

Numerical Methods Lab (C) RMA403

Explain the methods of Numerical Methods Lab (C)
GA1 3 (verb: explain, skill: Numerical Methods); GA2 2 (verb: explain); GA4 1 (skill: Numerical Methods)
Apply Numerical Methods Lab (C) to problems from science and economics
GA1 2 (skill: Numerical Methods); GA4 3 (verb: apply, skill: Numerical Methods, words: problems)
Learn the main techniques of Numerical Methods Lab (C)
GA1 1 (skill: Numerical Methods); GA4 1 (skill: Numerical Methods)

Programming in C RMA404

Explain the methods of Programming in C
GA1 2 (verb: explain); GA2 2 (verb: explain); GA6 2 (skill: C / C++ programming, words: programming)
Apply Programming in C to problems from science and economics
GA4 3 (verb: apply, words: problems); GA6 3 (verb: program, skill: C / C++ programming, words: programming)
Learn the main techniques of Programming in C
GA6 2 (skill: C / C++ programming, words: programming)

Statistical Methods RMA501

Prove the main theorems of Statistical Methods
GA1 1 (course: Correlation & Regression, Descriptive Statistics, Probability, Statistical Inference); GA3 2 (verb: prove)
Compute with the methods of Statistical Methods in applied problems
GA1 1 (course: Correlation & Regression, Descriptive Statistics, Probability, Statistical Inference); GA4 3 (verb: compute, words: problems)
Analyse problems that combine Statistical Methods with earlier courses
GA1 1 (course: Correlation & Regression, Descriptive Statistics, Probability, Statistical Inference); GA3 2 (verb: analyse); GA4 2 (words: problems)

Integral Transforms RMA502

Prove the main theorems of Integral Transforms
GA1 2 (skill: Integral Transforms & Fourier Analysis); GA3 2 (verb: prove)
Compute with the methods of Integral Transforms in applied problems
GA1 2 (skill: Integral Transforms & Fourier Analysis); GA4 3 (verb: compute, words: problems)
Analyse problems that combine Integral Transforms with earlier courses
GA1 2 (skill: Integral Transforms & Fourier Analysis); GA3 2 (verb: analyse); GA4 2 (words: problems)

Number Theory RMA503

Prove the main theorems of Number Theory
GA1 2 (skill: Number Theory); GA3 2 (verb: prove)
Compute with the methods of Number Theory in applied problems
GA1 2 (skill: Number Theory); GA4 3 (verb: compute, words: problems)
Analyse problems that combine Number Theory with earlier courses
GA1 2 (skill: Number Theory); GA3 2 (verb: analyse); GA4 2 (words: problems)

Graph Theory RMA504

Prove the main theorems of Graph Theory
GA1 2 (skill: Graph Theory); GA3 2 (verb: prove)
Compute with the methods of Graph Theory in applied problems
GA1 2 (skill: Graph Theory); GA4 3 (verb: compute, words: problems)
Analyse problems that combine Graph Theory with earlier courses
GA1 2 (skill: Graph Theory); GA3 2 (verb: analyse); GA4 2 (words: problems)

Metric Spaces RMA601

Prove the main theorems of Metric Spaces
GA1 2 (skill: Metric Spaces); GA3 2 (verb: prove)
Compute with the methods of Metric Spaces in applied problems
GA1 2 (skill: Metric Spaces); GA4 3 (verb: compute, words: problems)
Analyse problems that combine Metric Spaces with earlier courses
GA1 2 (skill: Metric Spaces); GA3 2 (verb: analyse); GA4 2 (words: problems)

History of Mathematics RMA602

Prove the main theorems of History of Mathematics
GA3 2 (verb: prove)
Compute with the methods of History of Mathematics in applied problems
GA4 3 (verb: compute, words: problems)
Analyse problems that combine History of Mathematics with earlier courses
GA3 2 (verb: analyse); GA4 2 (words: problems)

Project / Dissertation RMA603

Prove the main theorems of Project / Dissertation
GA3 2 (verb: prove); GA5 3 (skill: Research Project / Dissertation, words: project)
Compute with the methods of Project / Dissertation in applied problems
GA4 3 (verb: compute, words: problems); GA5 2 (skill: Research Project / Dissertation, words: project)
Analyse problems that combine Project / Dissertation with earlier courses
GA3 2 (verb: analyse); GA4 2 (words: problems); GA5 3 (skill: Research Project / Dissertation, words: project)

How this was worked out

Every course title and topic line was searched for the terms listed under each skill in the Undergraduate Mathematics taxonomy (48 skills). Course outcomes are not counted as evidence of a skill: they often look ahead to later study, and not every syllabus states them. Matching ignores case and punctuation, and the longest phrase wins (“partial differential equations” counts for PDEs, not ODEs). No AI or statistics are involved: each mark in this report traces back to a phrase in a syllabus.

Limits: a syllabus can teach a skill without naming it, and naming it does not show depth. Treat a gap as a question for the committee, not a verdict. Peer findings are only as good as the peer syllabi supplied.

Core and electives: a skill counts as core for a programme when its compulsory courses alone cover it, and as elective only when it depends on courses a student may choose. Gaps are judged on everything a programme offers; the split shows how firmly each peer teaches it.

Courses on a skill: a course counts as on a skill when its title names the skill, or when the skill appears in at least half of its topic lines (and in at least three). A programme without one teaches the skill inside courses about something else, which may mean a few examples rather than a unit of study.

Not yet analysed: industry skill demand.

Syllabus lines with no recognised skill

Either the line is generic, or the taxonomy is missing a term. Worth a look before trusting a “not found”.

RMA404: “Arrays and functions”