Data Science, Technology and Innovation

Courses

Further information about programme courses, including links to the full course outlines.

Courses are listed in alphabetical order below for the 2019/2020 Academic Year.  Click on the course codes below for full course outlines in the University’s Degree Programme Table.

NOTE: Courses are reviewed yearly and may not run in each Academic Year. The University's Degree Programme Table (DPT) is updated each Academic Year for the programme. Links for the 2019/2020 DPTs for each programme are available at the bottom of this page. 

Advanced Vision (INFD11002)

This module aims to build on the introductory computer vision material taught in Introduction to Vision and Robotics. The main aim is to give students an understanding of main concepts in visual processing by constructing or analysing several vision systems during the course of the lecture series and practicals. The 6 systems are for: rigid 2D part recognition, deformable 2D part recognition, rigid 3D part recognition from stereo data, rigid 3D part recognition from range sensing, target detection and tracking in video, and video based behaviour classification.

Engaging with Digital Research (PGSP11401)

The course will enable students to understand new emerging models of professional practice in business and policy making developing and deploying digital research methods and results. This will include collecting, curating, exchanging and analyzing of digitally-derived data, the use of research from digital environments, and the way this is leveraged turning this data into tools for active use and behaviour change.   This course will also address the development of transferable insights in managing cross-institutional and citizen collaboration in digital data collection and analysis. 

Introduction to Practical Programming with Objects (INFD11001)

This module is intended for students who have some previous programming experience, but would like to develop their ability to write complete, practical applications. Students with no programming experience should be able to complete the course, although this will almost certainly be challenging and will require additional time. The course uses an object-oriented approach, based around the Java language, but no previous experience of specific languages or technologies is assumed.

Introductory Applied Machine Learning (INFD11005)

This course is about the principled application of machine learning techniques to extracting information from data. The main area that will be discussed is supervised learning, which is concerned with learning to predict an output, given inputs. A second area of study is unsupervised learning, where we wish to discover the structure in a set of patterns, i.e. there is no output "teacher signal". The primary aim is to provide the student with a set of practical tools that can be applied to solve real - world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.

Managing Digital Influence (PGSP11391)

One of the most impactful effects of easier access to a larger proportion of data on an increasing number of phenomena is the use of rankings to assess all aspects of the performance of products and organizations based on customer feedback.  The course also offers a tutorial on Using Gephi as a tool for Measuring online Influence.

Medical Informatics (MCLM11037)

This course provides an introduction to data science in medicine, and more particularly to representing and interpreting data from areas across biomedicine and healthcare. It covers relational databases for medicine and healthcare, medical ontologies, statistical analysis of biomedical data, as well as some advanced topics in medical informatics, such as healthcare workflows and precision medicine. 

Message Passing Programming (INFD11011)

In the message-passing model the tasks are separate processes that communicate by explicitly sending each other messages. All parallel operations are performed via calls to some message-passing interface that is entirely responsible for interfacing with the physical communication network. This course uses the de facto standard for message passing, the Message Passing Interface (MPI), which is a library callable from C, C++ or Fortran. Parallel programs written using MPI can run on almost any system from a multicore laptop up to the world's largest supercomputers.

Natural Computing (INFD11007)

This module teaches you about bio-inspired algorithms for optimisation and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules. 

Neuroimaging: Common Image Processing Techniques 1 (NEME11006)

This core course aims to introduce the student to the major forms of image analysis commonly used in neuroimaging research. This ranges from the 'bread and butter' techniques like qualitative and quantitative assessment of whole brain and subregional brain volumes, simple and complex ways of measuring lesion size, region of interst measurements to assess tissue parameters as might be derived from diffusion-weighted or perfusion images, and simple tractography techniques.

Neuroimaging: Common Image Processing Techniques 2 (NEME11037)

This core course covers generally applicable image processing techniques in Neuroimaging, including DWI basic quantification, Tractography, Retinal image analysis, Registration techniques, Voxel based analysis, and Image segmentation.

Performance Modelling (INFD11008)

This course teaches various aspects of computer-aided modelling for performance evaluation of (stochastic) dynamic systems. The main focus is on stochastic modelling of computer systems and communication networks to assess performance characteristics such as throughput, response time etc.; however other dynamic systems such as manufacturing systems may also be considered. The central concept of the course will be that a model is as an abstract representation of a system which can be used as a tool to derive information about dynamic behaviour of the system. 

Practical Image Analysis 1 (NEME11054)

This practical, medical image analysis & processing course introduces MATLAB, an industry-standard operational platform for computational image analysis. Students will also work with related software, which interacts with MATLAB, & become familiar with 2D & 3D image operations, various medical image file formats, image enhancement, image alignment, & registration. Armed with this knowledge, students will tackle different medical image processing & analysis tasks, organised by topic & increasing difficulty as the course p0rogresses.

Practical Image Analysis 2 (NEME11055)

This practical, medical image analysis & processing course explores advanced MATLAB use & application. The course will help students assimilate & consolidate prior knowledge relating to image processing & analysis. Students will become familiar with 3D & 4D image operations, sophisticated image alignment & registration techniques, threshold-based image segmentation & classification, feature descriptors, machine learning applied to image segmentation & classification, 4D medical image analysis & processing, plus basic analyses of time series of volumetric image data.

Practical Introduction to Data Science (INFD11010)

This online course will provide a practical introduction to data science. It will have two broad themes, namely Data Management and Data Analytics. Data Science is an emerging field, which is becoming very important both in research, business and industry. The amount of data that is being generated and stored is greater than it has ever been, and this brings both challenges in terms of how you work with the data and - importantly - rewards in terms of new insight gained from analysing the data.

Practical Introduction to High Performance Computing (INFD11009)

The course will cover all the fundamental concepts that underpin modern HPC. The course is practical in the sense that you will explore these topics by running parallel programs on real HPC systems such as the UK national supercomputer ARCHER.

Probability and Statistics (MATH11204)

In this course you will learn the fundamentals of probability and statistics - the building blocks for all of data science. Covers both theory and practical aspects using R.

Public Health Informatics (GLHE11037)

This course provides a broad overview of the field, taking account of classic public health information delivery, core principles of epidemiology, health inequalities and health behaviour change, as well as the implications of massive linked datasets for research and policy, and the value of emerging mobile and social technologies for health surveillance. It will discuss the issues from an international perspective, with reference to global public health needs and the emergence of innovative digital systems and analytics.

Technologies of Civic Participation (PGSP11390)

The focus of this course is on understanding the current and potential uses of new TCPs by citizens and policy-makers in responding to mundane, everyday threats to social resilience (e.g. street crime, problems with community service delivery, environment, health, etc.), and appreciating how these activities (e.g. monitoring, informing, reporting) are linked to everyday life in the community.

Threaded Programming (INFD11012)

This course is a practical introduction to parallel programming using the threading model, which is commonly used on shared memory and multicore hardware. The majority of the course is focused on teaching the use of the industry standard OpenMP API.

The Use and Evolution of Digital Data Analysis and Collection Tools (PGSP11388)

In this course we will address the opportunities and challenges of a range of traditional and emerging digital research approaches and techniques focusing on the relevance of their applications from a user perspective. It will cover ethical, practical, legal, methodological and economic issues in theory and practice. 

Understanding Data Visualisation (PGSP11484)

In this course, we examine the visual aspects of data analytics and the emerging professional practices of turning numbers into pictures or, more specifically, into screen realities. Hosting contributions from key experts in the field, the course will provide students will skills to critically interpret the most popular data visualization techniques used by major information provider firms.

Dissertation Projects: 

Students must also complete a project (worth 60 credits) in the form of a written dissertation. This activity will allow students to draw from the combined experience of previous activities and interactions, and apply what they have learnt to a practical research question. The final submission will be expected to be at a level appropriate for an independent researcher and be a good indication of a student’s potential to go on to be a productive researcher in a relevant sub-discipline of Data Science.  (Available for students taking MSc programmes only.)

Data Science, Technology and Innovation Dissertation Project (MCLM11035)

Edinburgh Parallel Computing Centre Dissertation Project (INFD11013)

School of Informatics Dissertation Project (INFD11003)

Medical Informatics Specialism Dissertation Project (MCLM11036)

School of Social and Political Science Dissertation Project (PGSP11499)

2019/2020 Academic Year Degree Programme Tables:

PG Certificate: Data Science, Technology and Innovation (Online Distance Learning) (PgCert) (PTPGCDSTIN1U)

PG Diploma: Data Science, Technology and Innovation (Online Distance Learning) (PgDip) (PTPGDDSTIN1U)

MSc: Data Science, Technology and Innovation (Online Distance Learning) (MSc) (PTMSCDSTIN1U)

MSc with Medical Informatics Specialism: Data Science, Technology and Innovation with Medical Informatics Specialism (Online Distance Learning) (MSc) (PTMSCDSTIN2U)