Courses

Course description. A graduate-level treatment of the linear algebra, calculus, and probability that underpin modern analytics and finance. Students rebuild fluency with the mathematical objects they will encounter in MBA, MS-Finance, and MS-Analytics core courses.

Learning objectives

• Manipulate matrices, vectors, and eigenstructures with confidence.
• Differentiate and integrate multivariate functions used in optimisation.
• Reason rigorously about discrete and continuous probability distributions.
• Translate a finance or analytics problem statement into the right mathematical formulation.

Topics covered

• Linear systems, matrix decompositions, projection
• Single- and multi-variable calculus refresher
• Probability spaces, random variables, expectations
• Time value of money, discounted cash flow modelling

Overview & objectives. An immersion in the C language as a foundation for systems-aware engineering. Students leave able to read production C code and to reason about memory, pointers, and the runtime stack.

Learning objectives

• Write idiomatic, memory-safe C from scratch.
• Trace pointer behaviour, stack frames, and dynamic allocation.
• Use modular compilation, header files, and the build pipeline.

Representative topics

• Types, expressions, control flow
• Pointers, arrays, strings
• malloc, free, and ownership patterns
• Structures, file I/O, and standard-library routines

Course description. An introduction to integrated-circuit design with an emphasis on digital logic. The course bridges component-level reasoning to system-level architectures used in modern processors and ASICs.

Learning objectives

• Design combinational and sequential digital blocks.
• Reason about timing, fan-out, and propagation delay.
• Read and produce structural HDL for representative circuits.

Topics covered

• Boolean algebra and minimisation
• Combinational logic: adders, multiplexers, decoders
• Sequential logic: latches, flip-flops, finite-state machines
• Datapath and control-unit case studies

Course introduction. Differential equations and applied linear algebra for engineers. The course recovers the operational fluency expected of incoming graduate students in engineering disciplines.

Learning objectives

• Solve first- and second-order ODEs with engineering applications.
• Use Laplace transforms for system analysis.
• Apply Fourier methods to periodic signals.

Topics covered

• ODEs: separable, linear, and exact equations
• Laplace transforms and transfer functions
• Fourier series and basic PDE introduction
• Vector calculus refresher: divergence, curl, line integrals

Course description. Data structures and algorithms are the backbone of every CS sub-discipline. This course re-grounds students in the canonical data structures and the algorithmic patterns built on them.

Learning objectives

• Choose appropriate data structures for given problem constraints.
• Analyse time and space complexity using asymptotic notation.
• Implement and reason about classical algorithms.

Representative topics

• Arrays, linked lists, hash tables, balanced trees, heaps, graphs
• Sorting and searching, divide-and-conquer
• Greedy methods and dynamic programming
• Graph algorithms: BFS/DFS, shortest paths, minimum spanning trees

Course description. A practical course in applying Python and R to business problems. Students manipulate, model, and communicate insights from real-world business datasets.

Learning objectives

• Acquire, clean, and reshape tabular data using pandas and tidyverse.
• Fit and interpret regression and classification models.
• Communicate results with effective visualisations and reproducible reports.

Topics covered

• Data wrangling and EDA
• Linear and logistic regression, regularisation
• Classification trees and ensembles
• Reproducible reporting (Jupyter, R Markdown)

Course overview. Optimisation problems are everywhere in business — from inventory to scheduling to pricing. This course builds the mathematical and modelling fluency to formulate, solve, and interpret them.

Learning objectives

• Formulate decision problems as linear, integer, and network programs.
• Solve representative models with simplex and branch-and-bound.
• Conduct sensitivity analysis and interpret duality.

Topics covered

• Linear programming: formulation, simplex, duality
• Integer and mixed-integer programming
• Network flow models
• Stochastic programming primer

Overview & objectives. A foundation in the statistical reasoning expected of graduate-level coursework. Students learn to design, execute, and interpret data analyses on managerial and engineering questions.

Learning objectives

• Apply descriptive and inferential statistics to real datasets.
• Reason about uncertainty, sampling, and statistical power.
• Communicate quantitative arguments to non-technical audiences.

Topics covered

• Descriptive statistics, distributions, sampling
• Hypothesis testing and confidence intervals
• Correlation, simple and multiple regression
• Designed experiments and A/B testing

Course description. A graduate-paced survey of microeconomic and macroeconomic reasoning. The course gives students the conceptual vocabulary they will encounter in MBA core courses, finance electives, and policy seminars.

Learning objectives

• Reason about market structure, incentives, and equilibrium.
• Read macroeconomic indicators and policy debates with rigour.
• Apply marginal-thinking to managerial decisions.

Topics covered

• Supply and demand, elasticity, consumer surplus
• Production, costs, and competitive markets
• Imperfect competition and game-theoretic intuition
• National accounts, monetary and fiscal policy

Course description & learning objectives. An integrated introduction to three foundational modelling traditions: deterministic optimisation, probabilistic simulation, and decision analysis. Students learn how to choose the right tool for the right question.

Learning objectives

• Formulate problems in optimisation, simulation, and decision-tree form.
• Build, validate, and stress-test spreadsheet and code-based models.
• Communicate model assumptions, limits, and sensitivities.

Topics covered

• Linear and integer programming case studies
• Monte Carlo simulation patterns
• Decision trees and sequential decision making
• Model risk and validation

Course overview. A capstone in the writing, presenting, and visual-communication skills that international graduate students need on day one of their masters programme.

Learning objectives

• Structure technical arguments for academic and industry audiences.
• Produce clear figures, slides, and reports under realistic deadlines.
• Deliver a confident technical presentation in English.

Components

• Academic writing workshop and peer-review
• Slide and figure design
• Mock conference / interview talks
• Final portfolio: written report + recorded talk