CODE 
SSA5015 

TITLE 
Mathematics and Statistics for Astroinformatics 

LEVEL 
05  Postgraduate Modular Diploma or Degree Course 

ECTS CREDITS 
5 

DEPARTMENT 
Institute of Space Sciences and Astronomy 

DESCRIPTION 
Students will be exposed to the mathematical and statistical methods necessary for data science. In particular Mathematics and Statistics for Astroinformatics will lay the foundational methods for the rest of the master's programme. Therefore the studyunit focus has a level of flexibility depending on what is required for the other studyunits.
The studyunit will focus on the following topics giving both the basics and an introduction to the advanced level of each subject.
Statistics:  Linear regression  Probability  Interval estimation  Tests of hypotheses  Nonparametric methods  Analysis of variance  Stochastic Methods  Bayesian Inference
Mathematics:  Signal Processing  Advanced calculus  Vector Spaces  Advanced linear algebra
Studyunit Aims:
The main goal of this studyunit is to introduce the students to the advanced mathematical and statistical methods required for astroinformatics. The aim of the studyunit is to lay the foundations for the rest of the programme. This means giving the correct mathematical and statistical introductions for the other studyunits with a focus on the project.
Various methods will be introduced throughout the course of this studyunit and these will strengthen the students' problem solving skills in terms of analysis and statistical treatment. Besides giving an introduction to advanced mathematics in general, the studyunit will present a number of methodological treatments in statistical optimization of big data problems with analysis and processing. This will be further ingrained by means of the hands on assignment where students are asked to work through a big data problem.
Learning Outcomes:
1. Knowledge & Understanding:
By the end of the studyunit the student will:  have an advanced understanding of signal processing with a focus on discrete systems;  understand the problem of big data analysis;  have a deeper understanding of certain mathematical methods that can be applied to large dataset analysis;  understand how to apply and differentiate between different statistical methods used for big data problems;
2. Skills:
By the end of the studyunit the student will be able to:  demonstrate an advanced knowledge of mathematical methods such as calculus and vector analysis;  change between physical problems and signal processing problems;  apply statistical inference on large datasets;  estimate errors on large datasets.
Main Text/s and any supplementary readings:
 Mathematical Methods for Physics and Engineering: A Comprehensive Guide, K. F. Riley, M. P. Hobson, S. J. Bence (Cambridge University Press, 2006).  Modern Statistical Methods for Astronomy: With R Applications, Eric D. Feigelson, G. Jogesh Babu (Cambridge University Press, 2012).


STUDYUNIT TYPE 
Lecture and Tutorial 

METHOD OF ASSESSMENT 
Assessment Component/s 
Resit Availability 
Weighting 
Project 
Yes 
20% 
Examination (2 Hours)

Yes 
80% 


LECTURER/S 
Jackson Said


The University makes every effort to ensure that the published Courses Plans, Programmes of Study and StudyUnit information are complete and uptodate at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the studyunit description above applies to the academic year 2017/8, if studyunit is available during this academic year, and may be subject to change in subsequent years.

22 October 2017
http://www.um.edu.mt/issa/studyunit