MATHEMATICS OF DATA SCIENCE - 2022/3
Module code: MATM065
This module will introduce the subject of data science. It will start with the role of data in society as motivation, with the view towards enhancing global and cultural capabilities in understanding how usage of data shapes our society, and then move to the core of the module which is mathematical methodology for data analysis, providing students with the underpinning mathematics that drive data algorithms, so as to strengthen the resourcefulness and resilience of the students. The topics will be wide ranging with a focus on the Surrey brand of data, as research into data is part of the department research agenda, and then the module goes full circle and shows how all this theory drives data algorithms at all levels of society.
SANTITISSADEEKORN Naratip (Physics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 85
Lecture Hours: 33
Guided Learning: 32
Prerequisites / Co-requisites
1. Introduction: the role of data in society, data science, and big data. The concept of learning from data. Mathematical preliminaries such as the background linear algebra, statistics, eigenvalues/eigenvectors, principal components, matrix calculus, Moore-Penrose inverse, Rayleigh-Ritz quotient and positive matrices. The most important introductory topic is the singular value decomposition (SVD). The Introduction will also include some motivating examples that show what can be achieved by analysing data.
2. Data assimilation: introduction and motivation; relevance to linear dynamical systems. The theory will be developed in the context of the Kalman filter theory. Some operational drawbacks including ill-conditioning will be discussed.
3. Networks and clustering: introduction with familiar examples such as mobile phone networks, social media networks, and evolution of networks. Theoretical constructs such as the network Laplacian, Cheeger's constant and network clustering (spectral clustering and K-mean clustering).
4. Machine learning and classification: introduction the classic machine learning and the concepts of classification and regression. Supervised and unsupervised learning from data, the construction of neural networks, and finding patterns in data using linear discriminant analysis, principle component analysis, Gaussian mixture model and multivariate regression.
|Assessment type||Unit of assessment||Weighting|
|School-timetabled exam/test||In-semester test (50 minutes)||20|
|Examination||Examination (2 hours)||80|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
¿ Understanding of fundamental concepts and ability to develop and apply them to a new context.
¿ Subject knowledge through recall of key definitions, formulae and derivations.
¿ Analytical ability through the solution of unseen problems in the test and examination.
Thus, the summative assessment for this module consists of:
¿ One two hour examination at the end of the semester, worth 80% of the overall module mark
¿ one fifty minute class test worth 20%
Formative assessment and feedback
Students receive written feedback via the marked class test. The solutions to the class tests are also reviewed in the lecture. Un-assessed courseworks are also assigned to the students, and a sketch of solutions to these are provided. Verbal feedback is provided during lectures and office hours.
- The module aims to cover the range of data science and underpinning mathematics required to understand the key data algorithms in operation today.
|001||Demonstrate understanding of the data assimilation and its role in improving forecasting||K|
|002||Understand the concepts of networks and clustering, along with basic theory, and examples||CKT|
|003||Understand how large data sets can be used to identify a model, and the theory of data driven modelling||CK|
|004||Understand classic machine learning, and the evolution into deep learning and the search for patterns in data||CK|
|005||Being able to use more advanced regression analysis, regularisation under the energy norm and L1 regression||CT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Teaching is by lectures, 3 hours per week for 11 weeks. Lecture notes are provided. Learning takes place through lectures, exercises and class tests. Blackboards and whiteboards are used for real-time presentation. Supplementary notes provided via SurreyLearn. Periodic special lectures are devoted to discussion of example sheets.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MATM065
This course guides students through fundamental ideas in data analytics, such as data assimilation and machine learning. The mathematical theory underpinning data science will strengthen the resourcefulness and resilience of the students, for, their understanding will prepare students to be adaptive and deal with new problems that might arise, while the applications in implementing the mathematical ideas will enhance their employability.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.