The following is a brief overview of the modules taken in Senior Sophister year. Current students should refer to my.tcd.ie for full details, including assessment criteria and learning outcomes.
|ST4001 Final Year Project|
|ST4003 Data Analytics|
|ST4004 Management Science in Practice|
|ST4500/02 Strategic Information Systems|
The aims of the project are to integrate the theoretical and practical knowledge of the student across all of the years of their study and provide a practical demonstration of their capability in executing a challenging and large-scale project for a real world client.
The aim of the module is to introduce the students to a set of techniques including classification trees, neural networks, ensemble methods and support vector machines. Some techniques will be discussed in detail whilst a brief overview will be given for others. Methods to evaluate models will also be discussed.
This course will look into some of the topics covered in the earlier management science courses at greater depth, with emphasis on how the methods can be practically implemented, principally through Excel. The module covers: Performance Measurement; Risk Management; Simulation in complex modelling; Parametric elicitation; Supply Chain Management; Multi-objective, Multi-Criteria decision making; CPM and PERT analysis; Stochastic linear programming; Decision theory; Dynamic programming; Utility of money; Futures and options.
The objective of this module is to present students with a broad overview of the business and social impacts of contemporary developments in information systems and technology and to enable students to think critically about business and societal impacts and implications of ICT today and going forward. Specific topics addressed in this module will be drawn from a range of topics including, but not limited to: Enterprise resource planning systems; Customer relationship management systems; Knowledge management; IS management and governance; Outsourcing; IS evaluation; Strategic IS planning; IS project management; e-Government; e-Democracy; The information society; e-Business infrastructures; Mobile business and location awareness; Privacy and trust; Enterprise architectures; Service oriented architectures; Open systems; Emerging technologies; and Technology forecasting.
Note: ST4502 has to be taken by students who are taking a 15 ECTS elective such as BU4530 Financial Reporting and Analysis.
This module aims to:
- describe the professional area of work that is concerned with managing employees and their work in organizations;
- contextualize the practice of HRM in the Irish/EU employment law and industrial relations environment;
- expand upon the practices of HRM ( recruitment, training , reward , evaluation) in organizations,
- understand the importance of national, environmental and strategic context for the creation and implementation of HR strategy in organizations.
This module is a module in financial management. It will cover: Bond pricing; Types of fixed interest instruments; Duration and convexity; Bond portfolio management; Derivatives Markets and Commodity Markets.
Topics covered include:
- Historical evolution of finance
- Bubbles, Crashes and Cycles in finance
- Why there is no ‘right’ capital structure
- Why pay dividends?
- The market for corporate control
The aim of this module is to develop students' understanding of external financial reporting (principally by publicly quoted companies). The module is designed to follow the accounting principles module BU2520a and to develop students' understanding of external financial reporting (principally by publicly quoted companies).
This module builds on the foundations of EC2010 Intermediate Economics, developing some of the topics from that module, and introducing some new ones.
Module 1 covers Microeconomics: topics include general equilibrium and welfare; consumer's surplus, and extensions; risk and insurance; the portfolio problem; intertemporal choice; business financial decisions; Modigliani and Miller theorems on capital structure.
Module 2 covers Macroeconomics: intertemporal consumption and labour supply; investment theory; money demand; the analysis of business cycles; monetary policy; fiscal policy.
This module analyses, at both a practical and theoretical level, the process of investment in financial markets. Its aims are to introduce students to the various types of financial instruments in common use and to the economic theories that explain how they are priced. The types of securities considered include interest-bearing securities, equities and derivatives (options, futures, etc.). The focus for the first half of the module will be on fixed income securities and derivatives. As we will see, the principles to be discussed and the analytical tools to be presented have a much wider application in making decisions under conditions of uncertainty. Students are also required to complete a project involving the collection and analysis of financial data. The second half of the module explores how financial markets operate and how securities are bought and sold. The trade-off between higher average returns and more `risky' pay-offs is then discussed. The problem of determining an optimal investment strategy, given beliefs about the probability distribution of returns, is also addressed. Other issues considered include the informational efficiency of financial markets and systematic pricing failures, the role of behavioural biases, and the relative usefulness of fundamental analysis and technical analysis in predicting price movements. This module does not assume previous knowledge of financial economics and for the most part the level of mathematics and statistics does not extend beyond SF Maths and Stats. Students should note, however, that this is an analytical economics module that makes constant use of tools derived from mathematical and statistical concepts. Students interested in working in areas related to investment and finance are likely to find the course of value for their career.
Stochastic processes and in particular Gaussian, Poisson and Markov Models are the central examples of “stochastic processes”. Gaussian processes, in combination with Hidden Markov have become central tools in statistics and machine learning. They are used for smoothing, de-noising; and generally for determining structure in noisy signals and using this for prediction. This course will provide simple examples, some of which will be extended and applied in ST3454.
This course introduces different statistical modelling used for inference on Space and/or Time processes that have applications in engineering, geostatistics, finance etc. Pre-requisites: Solid knowledge in mathematics and statistics required e.g. on Linear algebra, Integration and differentiation, expectation operator.
This module aims to introduce statistical inferential approaches by means of probabilistic computation. Specific methods will be explored to illustrate these approaches.
This module will describe several topics of a more advanced nature in probability modelling and statistics.
This module provides a general introduction to operations management of manufacturing systems. It will explore strategies for operating and optimising the production of products in different varieties and volumes with limited resources and in competitive environments. The impacts of design decisions on manufacturing performance and the physical organisation of plants are explored through various DFM and plant layout strategies.
Formal project management methods will be introduced reflecting the growing use of continuous improvement through project management.
This module introduces the fundamentals of technology entrepreneurship. It will cover the process technology entrepreneurs use to start companies. This involves taking a technology idea and finding a high-potential commercial opportunity, gathering resources such as talent and capital, figuring out how to sell and market the idea and managing rapid growth.
Students will understand the main issues underlying the usability of systems, and the main techniques and processes for interface design and evaluation. They will also gain a basic understanding of the theories which account for human performance. Specific topics addressed in this module include: Usability User capabilities; Interaction models; User interface design process; Task analysis; Evaluation; User Interface Architectures; Co-operative work and groupware; Cognitive modelling; Human error; Interaction in context/Knowledge in the world views.
Note: option choices may be subject to change and, due to timetable constraints, students may not always get their first choice.
Note: module overviews may be subject to change