This course introduces the basic techniques of demographic analysis. Students will become familiar with the sources of data available for demographic research. Population composition and change measures will be presented. Measures of mortality, fertility, marriage and migration levels and patterns will be defined. Life table, standardization and population projection techniques will also be explored.
This module is not Computational Statistics (statistical methods that use computation). Rather it is an introduction to “Programming”. The module's aim is to introduce to students software such as MATLAB, R, Python, etc.. ,programming in order to implement numerical methods to solve problems in applied mathematics and statistics.
References
MATLAB
1). Amos Gilat, MATLAB: An introduction with Applications. John & Sons, Inc., Sixth Edition, 2017.
2. Steven Chapra, Applied Numerical Methods with MATLAB for Engineers and Scientists.
R
1). Robert I. Kabacoff, R in Action Data analysis and graphics with R. Second edition, 2015.
2). Basics
An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics by W. N. Variables, D. M. Smith and the R Development Core Team.
Course's aim
The aim of this module is to provide the students with deeper knowledge of linear algebra for the use in later courses and in Engineering and mathematical-Physics problems. It deals with linear maps between finite dimensional vector spaces, their representations and their applications. The course is made of four main chapters: Endomorphisms, Euclidian spaces, Unitary spaces and Jordan forms. Each chapter contains a series of exercises and at the end of two chapters an assignment is provided.
The aims of this module is to introduce to the students the basics of probability theory that are necessary to understand statistical inference. Topics covered are counting techniques, basic concepts of probability, conditional probability, random variables, probability distributions, joint and marginal distributions, conditional distributions, expectations, and moment generating function