2023/24 | Engineering, Mathematics and Physical Sciences Intranet

MODULE TITLE	Learning From Data (Professional)	CREDIT VALUE	15
MODULE CODE	COMM416DA	MODULE CONVENER	Unknown

DURATION: TERM	1	2	3
DURATION: WEEKS	0	0	11

Number of Students Taking Module (anticipated)	90

DESCRIPTION - summary of the module content

One of the primary aims of data science is to effectively use data to make better decisions. This module will introduce you to machine learning and statistical methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.

Pre-requisite modules: COMM415DA Fundamentals of Data Science (Professional) Co-requisite modules: None.

This module is a part of the dual-qualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes.

The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.

AIMS - intentions of the module

This module aims to provide you with some of the main ideas of machine learning and statistical modelling in a data science context. It will provide a grounding in the theory and application of some machine learning methods for classification, regression, and unsupervised learning including clustering and dimensionality reduction. We will also discuss the details of specific methods for classification, clustering, and for visualising complex datasets.

In addition to its academic aims as part of the programme, this module has specific aims as part of the Level 7 Research Scientist Apprenticeship. The full list of Knowledge, Skills and Behaviours that must be demonstrated to complete the Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/research-scientist-v1-0.

This module will deliver content that may be used to evidence the Knowledge, Skills and Behaviours set out below. Primarily: K1, K4 and K5. Secondarily: S1, S2 , S6 and S7.

Knowledge (K), Skill (S) or Behaviour (B)

K1: Subject specific knowledge: A deep and systemic understanding of a named / recognised scientific subject as found in an industrial setting, such as biology, chemistry or physics, found in the nuclear, food manufacture, pharmacology or energy production sectors, at a level that allows strategic and scientific decision making, while taking account of inter relationships with other relevant business areas / disciplines.

K4: Research methodologies: Methodologies appropriate to the sector and how to formulate and apply a hypothesis. Appropriate application of scientific process. The unpredictability of research projects and the need to adapt and adjust daily planning needs to accommodate new developments.

K5: Data analysis and evaluation: Statistical analysis techniques, numerical modelling techniques and how they are applied in context. How to interpret and categorise data to make informed and objective decisions against the goals and targets of the project. How to evaluate and interpret the data and associated analysis against company objectives.

S1: Scientific Knowledge: Apply a range of advanced, new and emerging practical and experimental skills appropriate to the role (e.g. chemical synthesis, bio analysis, computational modeling).

S2: Data Collection and Reporting: Capture and evaluate data critically drawing a logical conclusion, e.g. Case Report Forms, Data Management Plans, Data Review Plans, edit checks and User Acceptance Testing Plans.

S6: Critical Thinking: Conceptualise, evaluate and analyse information to solve problems.

S7: Research and dissemination: Frame research questions and methodology drawing from current sources e.g., literature and databases. They can produce intellectual insight and innovations in their own discipline to be shared with colleagues, peers and wider stakeholders internal and external to the business.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Understand concepts of supervised and unsupervised learning and different methodologies for applying machine learning and statistical modelling in each case;

2. Able to pre-process data to make it suitable for analysis;

3. Apply simple supervised and unsupervised pattern recognition and machine learning techniques to solve a wide range of problems;

4. Analyse novel pattern recognition and classification problems, establish models for them and write software to solve them.

Discipline Specific Skills and Knowledge

5. Understand different approaches to problem-solving in data science;

6. State the importance and difficulty of establishing principled models for pattern recognition;

7. Use Python and R for scientific analysis and simulation of real data.

Personal and Key Transferable / Employment Skills and Knowledge

8. Identify the compromises and trade-offs that must be made when translating theory into practice;

9. Critically read and assess research papers;

10. Conduct small individual research projects.

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics (with associated exercises and seminar discussions):

Taxonomy of problems and approaches in machine learning and statistical modelling
Data description and pre-processing
Probabilistic classification
Clustering and dimension reduction
Linear and logistic statistical models
Model assessment, cross-validation, hypothesis
Testing Bayesian learning
Linear support vector machines
Clustering (hierarchical and partitional)
Principal component analysis

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	40.00	Guided Independent Study	110.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities	40	Guided Independent Study	110	Placement / Study Abroad	0

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Feedback on practical work	12 hours	1-8	Oral

SUMMATIVE ASSESSMENT (% of credit)

Coursework	80	Written Exams	20	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Written exam	20	6-8 multiple-choice questions	1-8	Written
Individual technical report	80	3000 words	1-10	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
In Class Test	Written exam	1	Within 8 weeks
Individual technical report	Individual technical report	2-10	Within 8 weeks

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Reading list for this module:

Type	Author	Title	Edition	Publisher	Year	ISBN	Search
Set	Haykin, S	Neural Networks: A Comprehensive Foundation	2nd	Pearson	1999	000-013-908-385-3	[Library]
Set	Christopher Bishop	Pattern Recognition and Machine Learning		Springer	2007	978-0387310732	[Library]
Set	Webb, A.	Statistical Pattern Recognition	2	Wiley	2002	0-470-84513-9	[Library]
Set	Hastie T., Tibshirani R. & Friedman J.	The Elements of Statistical Learning: Data Mining, Inference, and Prediction	2nd	Springer	2009	978-0387848587	[Library]

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	COMM415DA
CO-REQUISITE MODULES

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Tuesday 06 August 2019	LAST REVISION DATE	Tuesday 24 January 2023

KEY WORDS SEARCH	data science, machine learning, statistical modelling, data visualisation

Computer Science

COMM416DA - Learning From Data (Professional) (2023)