| CODE | SSA5015 | |||||||||
| TITLE | Mathematics and Statistics for Astroinformatics | |||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | |||||||||
| MQF LEVEL | 7 | |||||||||
| 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 study-unit focus has a level of flexibility depending on what is required for the other study-units. The study-unit 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 Study-unit Aims: The main goal of this study-unit is to introduce the students to the advanced mathematical and statistical methods required for astroinformatics. The aim of the study-unit is to lay the foundations for the rest of the programme. This means giving the correct mathematical and statistical introductions for the other study-units with a focus on the project. Various methods will be introduced throughout the course of this study-unit 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 study-unit 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 study-unit 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 data-set analysis; - understand how to apply and differentiate between different statistical methods used for big data problems; 2. Skills: By the end of the study-unit 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 data-sets; - estimate errors on large data-sets. 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). |
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| STUDY-UNIT TYPE | Lecture and Tutorial | |||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Jackson Said |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date 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 description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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