The following is a brief overview of the modules taken in Senior Freshman year. Current students should refer to my.tcd.ie for full details, including assessment criteria and learning outcomes
The purpose of this course is to give students experience in advanced computer applications. This will include the advanced applications of Excel. The course will introduce students to database technology using Microsoft Access. Students will use Visual Basic for Applications (MS Office 2010).
Regression is probably the most widely used tool in statistics. When students have successfully completed this module they should: understand the concepts involved in simple and multiple linear regression analysis; understand how to use MINITAB software for regression; understand the pitfalls in analysis and understand its limitations.
This module covers a range of subjects in management science at an introductory level. The objectives of the module are to give students an overview of the subject, to teach important basic techniques and introduce systematic thinking about problems. The first semester starts with an introduction to problem solving and models and moves on to cover the time value of money, classic network problems, inventory control and time series forecasting and graphical linear programming. The second semester develops ideas in linear programming and introduces the simplex method. It will cover the basic transportation and allocation algorithms and introduce the basic ideas of game theory and decision analysis.
This module develops several important ideas in statistical analysis making use of some of the ideas introduced in ST2004. It acts as a bridge to the sophister years by introducing the fundamental ideas that are used in the more advanced statistics modules that will take place then.
The module will cover the derivation of the confidence interval and tests of hypothesis for normal data; the difference between a confidence interval and a prediction interval; the Central Limit Theorem and what it says about confidence intervals and tests of hypothesis; the bootstrap approach to confidence intervals and tests of hypothesis; introduction to maximum likelihood estimation and computation through Excel; the q-q plot and transforming data to make it more Gaussian; introduction to multivariate distributions; and statistical reasoning: bias in statistical studies (selection bias, Rubin's propensity).
This course is based on developing and solving mathematical models of real life problems. In the first semester, students receive a theoretical introduction to the fundamental elements of a mathematical model. Modelling techniques are taught to solve problems in many domains. In the second semester students are introduced to the concepts, ideas and techniques involved in simulation.
These modules are a natural continuation of the Junior Freshman Modules MA1E01 Engineering Mathematics I and MA1E02 Engineering Mathematics II.
Engineering Mathematics III introduces students to further fundamental ideas and methods of mathematics for engineering, covering the areas of multivariate calculus, integration and Laplace transforms. The aim of the module is to provide the necessary background and to teach the students to use it efficiently.
Engineering Mathematics IV introduces and illustrates the fundamental ideas and methods of linear algebra and fourier analysis. The module also introduces the concept of n-dimensional vectors and shows their role and importance in practice; shows the interrelations between linear systems, linear transformations and their matrices. The module aims to promote mathematical confidence and sensibility and to enable students to apply their knowledge to new situations.
The overall goal of these modules is that students obtain a sound understanding of 'money in organisations and – more specifically – learn how to judge (a) whether organisations are performing well and are financially healthy or (b) whether there are weaknesses in their financial performance/financial structure.
The modules deal with the construction and interpretation of five key financial statements – the balance sheet, the income statement, the statement of changes in equity, the statement of comprehensive income and the cash flow statement. Underlying concepts relating to matching, income measurement and asset valuation are explored in detail and the principles of sound financial management are developed as the modules progress.
This module introduces fundamental concepts and techniques of modern finance. It starts with reviewing the nature and role of financial markets, institutions and securities. The module proceeds with the presentation of the key tools used by financial managers and investors in analysis and decision making. The theoretical models and assumptions underlying the development of modern financial techniques will also be examined. On completion of the module students should be able to understand the principles underlying the working of most financial markets, to carefully evaluate investment opportunities and understand associated risks
The aim of the module is threefold:
1. To teach effective programming and problem solving, using a core toolset of classical algorithms and data structures.
2. To introduce the methods for evaluating the performance and requirements of programs written by the students
3. To promote effective software engineering by using well-established techniques for code modularity, structuring, debugging and readablity, such as Design by Contract, and unit testing.
Note: module overviews may be subject to change