BACHELOR OF SCIENCE HONOURS DEGREE IN APPLIED MATHEMATICS AND COMPUTATIONAL SCIENCE (HMTCS)
PROGRAMME OVERVIEW
The purpose of this programme is to provide a solid foundation and skills in the basic subjects of mathematical knowledge and its applications in the big data world. In addition to the enhanced career prospects that can be gained by taking this programme, it also allows complementary scientific training in computational mathematics with further studies in other scientific fields, extending the range of knowledge and skills of the students and, consequently, increasing the possibility to enter the labour market.
ENTRY REQUIREMENTS
For all entry pathways candidates must have at least five Ordinary Level subjects/ National Foundation Certificates including English Language, Mathematics and a Science subject at grade C or better.
| Normal Entry: | At least two Advanced Level passes including Mathematics or its recognized equivalents (Mechanics, Statistics, Pure Mathematics) plus at least five Ordinary Level passes/ National Foundation Certificates including Mathematics and English Language OR National Certificate. |
| Special Entry: | National Diploma or Higher National Diploma. |
| Mature Entry: | See General Academic Regulations |
CAREER OPPORTUNITIES AND FURTHER EDUCATION
Employability
After students graduate, they are employable in the following areas: Actuarial sciences; Financial Institutions: (banks, building societies, insurance companies and pension funds) as market or financial analysts, Brokers, Business Advisors, Entrepreneurs; Meteorology (forecasting); Network Managers, Manufacturing industry (production/operation management); Industrial Research; Research and Project Management in NGOs, research stations, and many other areas of applications; Mining Sector; Schools and Universities and many other relevant areas of the economy.
Further Studies
Master’s and doctoral studies in applied computational mathematics or in related interdisciplinary programmes.
PROGRAMME STRUCTURE
The MBK modules are in bold type and they comprise 80% minimum body knowledge and the rest comprise the 20% additional modules.
Level 1 Semester 1
Code Module Description Credits
HMAT131* Calculus 1 12
HMAT132* Linear Algebra 1 12
HMTCS131* Introduction to Machine Learning 12
HMTCS132 Web Database systems 12
CS131* Communication Skills 12
HCSCI132 Principles of Programming Languages 12
Level 1 Semester 2
HMAT139* Mathematical Discourse and Structures 12
HMAT140* Mathematical Computing and Simulation 12
HMAT133 Linear Algebra II 12
HSTA139* Probability and Statistics 12
HMTCS 133 Web Programming 12
Annual Credits 144
Level 2 Semester 1
Code Module Description Credits
HMAT231* Ordinary Differential Equations 12
HMTCS231 Information Security and Visualisations 12
HMTCS232 Advanced Programming 12
HMAT241* Analysis 1 12
HMAT223* Algebra 12
GS231* Gender Studies 12
Level 2 Semester 2
HMAT248* Numerical Methods 12
HMAT210 Calculus II 12
HMAT233* Introduction to PDEs and Fourier Series 12
HMAT249* Complex Variables 12
HMAT244* Vector Calculus 12
TCNP231* Technoprenuership 12
HMTCS270* Mini Project 12
Annual Credit 156
Level 3 Semester 1
Code Module Description Credits
HMTCS331* Work-Related Learning/Industrial Attachment Report 36
Level 3 Semester 2
HMTCS332* Academic Supervisor’s Report 24
HMTCS333* Employer’s Assessment Report 60
Annual Credits 120
Level 4 Semester 1
Code Module Description Credits
HMAT424* Optimization Techniques 12
HMAT433* Partial Differential Equations 12
HMTCS440* Mathematical Finance 12
Level 4 Semester 2
HMTCS441* Mathematical Computing for Life Sciences 12
HMAT442* Real Analysis 12
HMTCS470* Research Project 36
Elective Modules for Level IV
HMTCS442 Numerical Computations for Financial Modelling 12
HMTCS443 Coding and Cryptography 12
HMTCS444 Database Management and Administration 12
HMTCS446 Applications of Graph Theory 12
HMTCS447 Human Computer Interaction and Design 12
HMTCS410 Stochastic Processes for Applied Mathematics 12
HMAT436 Mathematical Programming 12
HMAT444 Mathematical Modelling 12
Annual Credits 120
NB: Students must choose at least two elective modules from the given list.
SYNOPSES
HMAT131 Calculus 1
Real number system, intervals, inequalities and their solutions, absolute value, functions, Limits
and continuity of single variable functions, Differentiation: rules of differentiation, differentiation
from first principles, L’Hopital’s Rule, Rolle’s Theorem, Mean Value Theorem of Differential
Calculus. The module also covers: Integration – indefinite and definite integrals, integration
techniques; substitution method, integration by parts, tabular integration, trigonometric
substitutions, reduction formulae, Mean Value Theorem of Integral Calculus.
HMAT132 Linear Algebra 1
Vectors: Basic properties and operations. Real and complex vectors in n dimensions Linear independence, bases for Rn and Cn,the scalar product of two vectors, vector product, geometrical representation of real vectors. Complex numbers: geometric representation, algebra. De Moivre’s theorem polynomials and roots of polynomial equations. Matrices and determinants: algebra of matrices, inverses, definition and manipulation of determinants, solutions of simultaneous linear equations, applications to geometry and vectors. Differential equations: separable, homogeneous, exact, integrating factors, linear equation with constant coefficients. Application: Use of practical big data in linear equations about business problems, tax problems, economic planner’s models, and input-output matrix for economic producing transportation problems, and interpret big data analytic problems. Explore real data using eigenvalues, and eigenvectors to discover information from the big data. Applications in structural engineering, control theory problem, vibration analysis problem, electric circuits problem, and advanced dynamic problem and others.
HMTCS131 Introduction to Machine Learning
Introduction to fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real life problems from the point of view of modelling and prediction: modelling and algorithmic techniques for machines to learn concepts from data extracting meaningful patterns from random samples of large data sets. Introduction to Python programming. Introduction to supervised learning (decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning), unsupervised learning (clustering, dimensionality reduction), and reinforcement. The module includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and temporal sequences. Application of Machine Learning (ML) to recent technologies such as autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition and social network analysis among others. Tasks: classification, regression, clustering, mixture models, neural networks, deep learning, ensemble methods and reinforcement learning. Optimization-based learning: loss minimization. regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbour, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Common paradigms: neural networks, kernel methods and support-vector machines, boosting, nearest neighbour classifiers Applications to data mining, human computer interaction. Additionally, the module will discuss evaluation methodology and recent applications of ML including large scale learning for big data and network analysis.
HMTCS132 Web Database Systems
An Introduction to Information Systems. Information gathering and requirements specification. An introduction to the main software design cycle phases: understanding the difference between requirements specification, analysis and design. An introduction to systems development using a structured methodology. Basic systems development with Data driven approach. The Unified Modelling Language (UML). ER modelling concepts, introduction to databases and SQL. Object-oriented approach to system development. Basic concepts of objects and OOAD. SQL and database software.
HMAT139 Mathematical Discourse and Structures
Logic, set theory, relations, functions, algebraic structures and graph theory. Sets: formulae,
propositions, Boolean algebra and its applications. Logic, mathematical reasoning and proof:
examples taken from various areas of mathematics. Relations: binary, n-ary, reflexive, symmetric,
transitive, equivalence relations and classes, partitions, order relations, inverse relations. Functions:
one-to-one, onto, inverse functions. Operations: binary, n-ary closed, associative, distributive.
Structures: sets with one or two binary operations: permutations, symmetry groups, modular
Arithmetic
HMAT140 Mathematical Computing and Simulation
Introduction to ideas of mathematical modelling: testing and validating a model. Review of essential concepts in statistics. Deriving and solving models with deterministic and probabilistic demand. Models with Poisson arrival, steady-state analysis, and cost analysis. Application to simple problems of Operational Research including business and physical systems (e.g. queueing, inventory control).
Using software such as spreadsheets for calculation and data presentation. Introduction to computer programming for mathematical work: routines, functions, control structures for selection and repetition. Designing a computer program, testing and debugging. Mathematical logic and truth tables. Introduction to mathematical software: MATLAB, Python, Mathematica, Minitab and R. Any other suitable mathematical software will be included. Introduction to software capable of performing simulation. Case Study: Group work to use Operational Research techniques to model and solve realistic business and analyse the output in a professional manner.
HMAT133 Linear Algebra II
Vector spaces, linear dependence and independence, bases and dimension. Inner product spaces. Basic definitions with examples, the notion of norm and distance, the Cauchy-Schwarz inequality, the Gram-Schmidt orthogonalisation process, orthonormal basis. Linear transformations. Operations on linear operators, algebra of operators. Eigenvectors, eigenvalues, orthogonality of eigenvectors, geometric and algebraic multiplicity of eigenvalues. Applications of diagonalisation of matrices, quadratic and bilinear forms, Jordan Normal form of a matrix, solutions of systems of differential equations. The Cayley Hamilton Theorem and its applications. Revision of the basic techniques for solutions of first and second order differential equations.
HSTA139 Probability and Statistics
Counting rules used in Probability: summation notation, product notation, random experiments, and basic principles of counting. Sets and events: operations with sets and subsets, De Morgan’s rules, sample spaces and events. Probability: the concept of probability, conditionally probability, law of total probability, properties of probability measure. Random variables: random variables and probability, discrete and continuous random variables. Special probability distributions: Expectation and moments: Chebyshev’s inequality. Generating functions: probability generating function and its properties, moment generating function and its properties, Normal approximation, Laws of large numbers. Joint probability distributions: discrete and continuous random vectors, independence, conditional distributions, covariance, and correlation. Bayesian inference with known priors, probability intervals, conjugate priors, Bayesian inference with unknown priors, Frequentist significance tests and confidence intervals, Resampling methods: bootstrapping, Linear regression. Application: Classifying data sets into categories that describe the shape of the data distribution. Graphical visualization ranging from simple graphics such as histograms, boxplots,and scatterplots to advanced graphics such as PCA projection plots, trellis plots, maps, etc. Students must use available software packages to explore big data using graphics to discover information from real data. Basic statistics, the definition of terms, data presentations, data descriptive measures of centre, position and dispersion
HMTCS133 Web Programming
Review of HTML/CSS and HTML Forms. Client / Server relationship – HTTP . Client-side scripting and CSS frameworks. Relational databases: creating, updating and normalisation in MySQL, Server-side PHP PDO programming. Website security including basic data encryption. Understanding of Content Management Systems. Accessibility and user-testing. Review of JavaScript. Introduction to Node JS and server-side JS Intro to Mongo DB and NoSQL ExpressJS, VueJS. Client / Server relationship – HTTP Accessibility and user-testing. Ethical, Legal and Social Issues.
HMAT111 Ordinary Differential Equations
Modelling with first order ODEs in population dynamics, and second order ODEs (mass-spring systems, RLC circuits). Methods of undetermined coefficients, reduction of order and method of variation of parameters. Existence and uniqueness of solutions, revision of continuous functions and Lipschitz conditions. Series solutions of ODEs, solutions near ordinary and singular points. Systems of linear first order ODEs, solution and stability. Differential equations of special functions. Laplace transforms and inverse Laplace transforms, applications to solving IVPs, Heaviside and Dirac functions.
Application: Use the practical big data for differential equations such as simple chemical conversion problems, growth of population problems, price of commodities models, Newton’s law of cooling problems, and many physical problems.
HMTCS231 Information Security and Visualisation
Fundamental principles of information security and concepts. Security risk assessment and controls for different systems and organizations. Algorithms and procedures covering cryptography and its applications. Principles of secure design. Approaches to cyber security. Fundamental concepts in information visualisation. Good design practices in information visualisation. Different types of information visualisation and the options for using them. Interactive tools for information visualisation and dashboards. Visual analytics for identifying trends and patterns in datasets. Practical experience of data exploration.
HMTCS232 Advanced Programming
Programming with threads; Using reflection; Creating software components; Test-driven development (e.g. with JUnit); Version control and build tools (e.g. Git); Creating Generic classes; Design patterns; Building libraries. Lambda expressions
HMAT241 Analysis 1
Revision of elementary functions including hyperbolic functions and their inverses. Limits and continuity of functions. Techniques of differentiation. Higher order derivatives. Applications to curve sketching and extreme value problems. Taylor series. L’Hopital’s rule. Methods of integration. The fundamental theorem of calculus. Applications of the integral. Complex exponentials. De Moivre’s theorem. Historical development of the real number system: Countability, cardinal numbers, existence of transcendental numbers. The real numbers as a complete, ordered field. Supremum axiom, Archimedean property, principle of monotone bounded convergence, nested interval theorem, Bolzano-Weierstrass theorem, convergence of Cauchy sequences. Limits and continuity of real functions. Boundedness theorem, intermediate value theorem, interval theorem, application to fixed –point theorem, Uniform continuity. Differentiability, Local extrema, Rolle’s theorem, mean – value theorem, L’Hospital’s rule, Leibnitz’s theorem. Taylor’s theorem. Applications to finding roots, curve sketching, classification of local extrema, and approximation by polynomials. The Riemann integral, Integrability, properties of the Riemann integral, the mean-value theorem for integrals, the fundamental theorem of calculus.
HMAT223 Algebra
Sets and relations, mapping, binary operation including closure of sets. Equivalence relations. Identities, inverse and zero divisors. Group theory (An introduction): Axioms and examples of groups. Subgroups. Homomorphisms and isomorphisms between groups. Automorphisms of a group. Vector Spaces (A more abstract and subspaces over arbitrary fields) Linear dependence and independence. Basic and Dimension. Linear conformation. Rings and Fields: Axioms and examples of rings and fields.
HMAT248 Numerical Methods
Introduction to simple numerical methods for solving problems in Mathematics and Science; Computer arithmetic and rounding errors; Root location in one- and higher-dimension (Newton-Raphson, Bisection), Numerical Linear Algebra (Gaussian elimination, LU decomposition, Gauss-Seidel method, Eigenvalue and Eigenvector iterative methods). Polynomial interpolation and splines; Solution of linear algebraic equations, scaled partial pivoting; Solutions to Ordinary Differential Equations including Initial Value Problems (Euler’s method, Runge-Kutta methods) and Boundary Value Problems (tridiagonal system and Thomas algorithm for tridiagonal matrices), Solutions to Partial Differential Equations (finite-difference methods). Examples from applications (e.g. engineering, economics, internet search engines, bio-sciences).
HMAT210 Calculus II
Theorems on differentiation, higher order derivatives and Leibnitz,s formula. The Mean value theorems: Rolles theorem, the Mean value theorem, the generalised mean value theorem, Taylor s theorem. Applications to maxima and minima, curve sketching, approximations and Newton’s Method. Leibnitz’s Theorem, Functions of several variables: limits, continuity. Differentiation of functions of several variables, Tailors theorem. Applications of maxima and minima problems, Lagrange multipliers. Multiple and triple integrals: change of order of integration transformations, Applications to finding area, volume, arc length, centroid, moments of inertia, etc. Series: tests of convergence, absolute and conditional convergence, series of functions, uniform convergence.
HMTCS231 Introduction to Partial Differential Equations and Fourier Series
Partial differential equations of mathematical physics and introduction to their study. Classification of second order partial differential equations in two independent variables. Derivation of the wave, Laplace and Poisson equations, method of separation of variables and Laplace transform techniques. Orthogonal sets of functions in an inner product space. Introduction to Hilbert spaces. Fourier sine and Fourier Cosine series. Discussion of convergence theorem. Integration and differentiation of Fourier series. Application of Fourier series to boundary value problems. Fourier series in two variables. The Fourier transform and its inverse. The convolution theorem. Applications. Bessel functions J(x). The zeros of J(X). Orthogonal sets of Bessel functions. Fourier Bessel series. Applications of the theory to the solution of partial differential equations will be stressed throughout.
HMAT224 Complex Variables
Analytic Functions. Cauchy-Riemann Equations. Conformal Mappings. Line integrals. Cauchy’s Integral Theorem and Formula. Power series. Taylor series, Laurent series. Zeros and singularities. The Residue Theorem. Evaluation of real integrals.
HMAT214 Vector Calculus
Brief review of line: surface and volume integrals and applications to work done, flax through surfaces etc. Derivatives of Vector Functions. Directional Derivatives. Gradient of Scalar Fields. Divergence and Curl of Vector Fields. Constrained extremal problems. Line and surface integrals. Green’s Theorem, Gauss’ Divergence Theorem, Stokes’ Theorem and their applications. Orthogonal Curvilinear Coordinates. The Laplacian in polar, cylindrical and spherical coordinates.
HMTCS270 Mini Research Project
The mini research project involves the specification, design and implementation of a piece of software or hardware or the use of existing software to develop some computational mathematical application. Work may be drawn from one of several proposed research themes and is guided by an academic supervisor. The mini research project aims to develop research, analytic and report writing skills.
HMAT424 Optimization Techniques
Formulations of combinatorial optimization problems, greedy algorithms, dynamic programming, branch-and-bound, cutting plane algorithms, decomposition techniques in integer programming, approximation algorithms. Nonlinear Optimization A course on the fundamentals of nonlinear optimization, including both the mathematical and the computational aspects. Necessary and sufficient optimality conditions for unconstrained and constrained problems. Convexity and its applications. Computational techniques and their analysis. Deterministic OR Models: An applications-oriented approach illustrating how various mathematical models and methods of optimization can be used to solve problems arising in business, industry, and science. Computational optimization methodologies underlying portfolio problems in finance. Computational linear algebra, determining derivatives, quadratic, and nonlinear optimization. The efficient frontier problem. Applications: Use and interpret practical big data in business/finance optimization, economic optimization and other economic producing problems, and interpret big data analytic problems.
HMAT433 Partial Differential Equations
Second Order PDE’s and Method of Separation of Variables derivation of the wave and diffusion equations, solutions of second order equations, initial and boundary conditions, solution of the wave equation, Laplace’s equation, wave equation and the heat flow equation. Parabolic Partial Differential Equations- equation for time-dependent heat flow, explicit method, Crank-Nicolson method, the Theta method, parabolic equations in Two or Three dimensions. Hyperbolic Partial Differential Equations, wave equation. Introduction to solution of PDEs using finite differences and finite elements. Solution procedures: elliptic equations — Green functions, conformal mapping; hyperbolic equations — generalized d’Alembert solution, spherical means, method of descent; transform methods Fourier, multiple Fourier, Laplace, Hankel (for all three types of partial differential equations); Duhamel’s method for inhomogeneous hyperbolic and parabolic equations.
HMTCS440 Mathematical Finance
Theory of interest rates: simple interest, compound interest, nominal rates of interest, accumulation factors, force of interest, present values, Stoodley’s formula for the force of interest, present values of cash flows. Basic compound interest functions: the equation of value and yield of a transaction, annuities certain, present values and accumulations, deferred annuities, continuously payable annuities, the general loan schedule, the loan schedule for a level annuity. Nominal rates of interest: annuities payable pthly, annuities payable pthly, present values and accumulations, annuities payable at intervals of time r, where r>1, the loan schedule for a pthly annuity. Discounted cash flow: Net cash flows, Net present values and yields, comparison of two investment projects, different interest rates for lending and borrowing, effects of inflation, the yield of a fund, measurement of investment performance. Capital redemption policies: Introduction to premium calculations, policy values, policy values when premiums are payable pthly, surrender values, paid up policy values and policy alterations, variations in interest rates, Stoodley’s logistic model for the force of interest, reinvestment rates. Application: use of practical big data in the financial sector.
HMTCS441 Mathematics Computing for Life Sciences
Population modelling – predator-prey models, dynamic modelling, simulation, analysis of behaviours. Lotka-Volterra equations and other population models. Modelling the spread of disease. The mathematics of DNA. The topology of viruses. Evolutionary concepts: kin selection, group selection. Mathematics of evolution: game theory, Hawk-dove models, evolutionary steady states. The Prisoner’s Dilemma as a model for the evolution of altruism. Tit for tat and other strategies for the Iterated Prisoner’s Dilemma. The Tragedy of the Commons. Modelling co-operative behaviour. Cycles of behaviour patterns. Punishment and reward. Trust and reputation. “Game Theory can save the world” (Nowak and Highfield). Climate change as a case study.
HMAT442 Real Analysis
Sets. Algebra of sets, Borel sets. Topology of real line. Outer measure on R, its properties. Measurable sets and Lebesgue measure. Properties of Lebesgue measure. The sigma algebra of measurable sets. The sigma algebra of Borel sets. Example of a non-measureable set. Measurable functions, equivalent definitions, properties. Convergence almost everywhere, convergence in measure. Lebesgue integral. Properties. Classes of integrable functions. Comparison with Riemann integral. Bounded convergence theorem. Lebesgue theorem. Fatou’s lemma. Monotone convergence theorem. Lebesgue convergence theorem. Absolutely continuous functions. Product measures. The Fubini theorem. Completion of product measures. Distribution functions. Integrating on product spaces. Introduction to linear vector spaces. Normed spaces. spaces, L∞space. The Holder and Minkowski inequalities. Convergence in LP. Comparison with other kinds of convergence. Measure spaces.
HMTCS442 Numerical Computations for Financial Modelling
Introduction to the mathematical models used in finance and economics with emphasis on pricing derivative instruments. Financial markets and instruments; elements from basic probability theory; interest rates and present value analysis; normal distribution of stock returns; option pricing; arbitrage pricing theory; the multi-period binomial model; the Black-Scholes option pricing formula; proof of the Black-Scholes option pricing formula and applications; trading and hedging of options; Delta hedging; utility functions and portfolio theory; elementary stochastic calculus; Ito’s Lemma; the Black-Scholes equation and its conversion to the heat equation.
HMTCS443 Coding and Cryptography
Introduction to coding theory. Parity checking, error detection and correction, code bounds. Examples of simple codes such as Hamming codes and Hadamard codes. Linear codes, polynomial codes and cyclic codes, including historical and modern examples. Modern applications such as Reed-Solomon codes. Introduction to cryptography. Historical ciphers, including substitution ciphers, stream ciphers (e.g. linear shift register) and block ciphers (e.g. DES/AES). Public-key cryptography, including the mathematical theory behind the ciphers (e.g. RSA, elliptic curves). Privacy, authentication, integrity and non-repudiation. Digital signatures and PKI infrastructures. Future developments, including quantum computing and quantum cryptography.
HMTCS444 Database Management and Administration
Exploring the database architecture; Creating the database; Managing the database instance; Managing database storage structures; Managing transactional processing and locking mechanism; Database security; Monitoring the database and using the advisors; Backup and recovery concepts; investigating, reporting, and resolving
problems.
HMTCS446 Applications of Graph Theory
Introduction to graphs and digraphs, walks, paths, trails and cycles. Definitions and characterization of classes of special graphs. Distance and connectedness measures. Eulerian and Hamiltonian graphs. Connectivity – Whitney’s theorem and Menger’s theorem. Planarity – Kuratowski’s theorem and Euler’s theorem. Colourability – 5-colour theorem, 4-colour theorem. Algorithms – Greedy algorithm, shortest path algorithms, Fleury’s algorithm. NP completeness. Applications in computing, engineering, finance, mathematical biology, social networks.
HMTCS447 Human Computer Interaction and Design
Design Process, Prototyping, Prototyping Tools, User Research, Research Methods, Evaluation of HCI solutions, Usability testing, Cognitive Psychology, Information Processing, HCI methodologies, standards and guidelines, Multi-Modal Interactions, Multi-Sensory Interactions, Tangible User Interfaces, Brain Computer Interfaces, Natural User Interfaces, Dialog Systems, Metaphors, Conceptual Models, Relevant legal, social, ethical and professional issues.
HMTCS410 Stochastic Processes for Applied Mathematics
Random variables, expectations, conditional probabilities, conditional expectations, convergence of a sequence of random variables, limit theorems, minimum mean square error estimation, the orthogonality principle, random process, discrete-time and continuous-time Markov chains and applications, forward and backward equation, invariant distribution, Gaussian process and Brownian motion, expectation maximization algorithm, linear discrete stochastic equations, linear innovation sequences, Kalman filter, various applications.
HMAT436 Mathematical Programming
Introduction to mathematical programming problems: Linear programming problem formulation; simplex method; Chmens and two phase techniques; sensitivity analysis; duality in LP; Dual simplex method; transportation and assignment methods; integer programming; dynamic programming; quadratics and separable programming; K T conditions for optimality.
HMAT444 Mathematical Modelling
This module introduces mathematical modelling in a variety of contexts including using Newton’s laws of motion, Newton’s law of gravitation, population models, exponential growth, density dependent growth, and predator-prey models. Deriving differential equations from big data; dimensional analysis; discrete models and difference equations: steady states and their stability; continuous models and ordinary differential equations: steady states and their stability; the slope fields and phase lines; applications of Linear Algebra (in lower dimensions): systems of linear ordinary differential equations; linear phase plane analysis and stability; electrical networks; vector algebra, vector geometry, vector equations, coordinate systems and vector differentiation; application in mechanics: Newton’s laws for a single particle in 3-D; conserved quantities; angular velocity, angular momentum, moment of a force; harmonic motion.
HCSCI132 Principles of Programming Languages
Refer to Department of Computer Science and Computer Engineering.
CS131 Communications Skills
Refer to Communication Skills Centre.
GS231 Introduction to Gender Studies
This module will empower the students with knowledge and skills that enable them to be gender sensitive in the University, workplace and in all their social interaction. Topics covered include: understanding gender, gender analysis, gender issue in Zimbabwe, redressing gender imbalances, empowerment and strategies for creating gender responsive environment. Students gain insight into accounts of gender studies in Science and Technology
TCNP231 Technoprenuership
Refer to Entrepreneurship Department.