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2020/21 Taught Postgraduate Module Catalogue
COMP5511M Principles of Data Science & Analytics
15 creditsClass Size: 80
Module manager: Dr Peter Tennant
Email: p.w.g.tennant@leeds.ac.uk
Taught: 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan) View Timetable
Year running 2020/21
Pre-requisite qualifications
Academic entry requirementsA 1st degree in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components (at least 2:1). We also consider working experience (two years or more) of research in a quantitative subject area. Non-graduates who: have successfully completed three years of a UK medical degree; are normally ranked in the top 50% of the year 3 cohort; and wish to take the MRes in Data Science & Analytics for Health as an intercalated programme, will also be accepted.
English language requirements
An overall score of 7.0 on IELTS (International English Language Testing System) with at least 6.0 in writing and no other skill below 6.5; from a TOEFL paper-based test the requirement is a minimum score of 600, with 4.5 in the Test of Written English (TWE); from a TOEFL computer-based test the requirement is a minimum score of 250, with 4.5 TWE; from a TOEFL Internet-based test the requirement is a minimum score of 100, with 25 in the "Writing Skills" score.
This module is not approved as an Elective
Module summary
The module is designed to provide students with a thorough grounding in the principals of planning, conducting, and critically reviewing data scientific research in applied contexts. By the end of the module, students will be confident with: the language and conventions of data science, calculating and interpreting measures of occurrence and association, designing and evaluating scientific studies in populations, identifying and appraising sources of bias, and using causal diagrams to support causal reasoning.Objectives
The module is designed to provide students with a thorough grounding in the principles of planning, conducting, and critically appraising data science research.Learning outcomes
Upon successful completion of the module the students will have demonstrated:
1. in-depth specialist knowledge related to: measures of exposure and outcome occurrence; measures of association and attribution; error, bias, precision and accuracy; prediction vs. causation – by successful engagement in the 5 Lectures and completion of assessment (i), see below;
2. a sophisticated understanding of concepts at the cutting edge of data science, including: the merits/weaknesses of descriptive, observational and experimental designs; inductive and deductive reasoning; refutation and replication; conditional exchangeability; and counterfactuals – by successful engagement in the 4 Tutorials and successful completion of assessment (i), see below;
3. proficient intellectual and practical analytical skills relevant for: constructing and interpreting causal models (charts and diagrams); identifying confounders, mediators and colliders; recognising sources of error and bias – by successful completion of the formative online assessment, and successful completion of assessment (ii), see below;
4. independent ideas and hypotheses, particularly concerning: future practices required to improve the reporting, validity and utility of data science – by successful engagement with the online learning materials and online discussion forum, and successful completion of assessment (ii), see below;
5. self-reflection with regard to the development of collegial, participative and professional relationships with peers, colleagues, trainers and hosts, including: scientific attitudes and behaviours; opportunities and challenges of interdisciplinary team science; and challenges to inclusive, open and engaged data science – by successful engagement in the 4 Tutorials and online discussion forum, see below;
and
6. an ability to keep abreast of current issues, new developments and professional advances as these impact upon: personal knowledge, understanding and skills; professional regulation and responsibility – by successful engagement with the online learning materials, formative online assessment, and online discussion forum, see below.
Skills outcomes
Recognise and practise the professional attitudes and behaviours needed for robust scientific research.
Syllabus
The module will be delivered through a blend of: face-to-face small group work and lectures; and online material (including feedback to support private study). The module will exploit web-based learning by providing: online material covering the basics of study design and statistical analysis relevant to each design; reusable learning objects, available as online audio-visual presentations (providing engaging synopses of data science); formative online assessments (delivered using Blackboard, offering student self-assessment through web-based material and audio-visual webcasts); supplementary reading from text books and notable articles (with weekly tasks linked to an online discussion forum to share issues arising from taught sessions and private study).
The course will cover the following topics:
(a) the scientific method, error and bias;
(b) measures of occurrence and risk;
(c) scientific study designs;
(d) an introduction to prediction, classification and causal inference; and
(e) the ‘hierarchy’ of scientific evidence.
Attainment of intended learning outcomes will be assessed using three separate assignments:
(i) two timed, open-book MCQ tests delivered over 7 days – 15% each; and
(ii) ~1,500-word extended answer questions – 70%.
The rationale for this strategy is to: (i) offer a phased re-introduction to academic assessment for mature and part-time students that can be scheduled around their other (professional and personal) commitments; and (ii) ensure a robust assessment of the learning necessary to progress to Module 2.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 5 | 2.00 | 10.00 |
| Tutorial | 4 | 2.00 | 8.00 |
| Independent online learning hours | 60.00 | ||
| Private study hours | 72.00 | ||
| Total Contact hours | 18.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Private study
The module will exploit web-based learning by providing: online material covering the basics of study design and statistical analysis relevant to each design; reusable learning objects, available as online audio-visual presentations (providing engaging synopses of data science); formative online assessments (delivered using Blackboard, offering student self-assessment through web-based material and audio-visual webcasts); supplementary reading from text books and notable articles (with weekly tasks linked to an online discussion forum to share issues arising from taught sessions and private study).Opportunities for Formative Feedback
Online formative assessments, delivered using Minerva after each teaching session (see above)Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Coursework 1 | 70.00 |
| Assignment | Coursework 2 | 15.00 |
| Assignment | Coursework 3 | 15.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
There is no reading list for this moduleLast updated: 22/01/2021 10:12:40
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