2023/24 | Engineering, Mathematics and Physical Sciences Intranet

MODULE TITLE	Introduction to Data Science (Professional)	CREDIT VALUE	15
MODULE CODE	COMM414DA	MODULE CONVENER	Dr Rudy Arthur (Coordinator)

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

Number of Students Taking Module (anticipated)	90

DESCRIPTION - summary of the module content

In this module you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling & visualisation, machine learning, statistical modelling, social network analysis and text mining. You will also explore the ways in which data science is transforming business and society. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

Pre-requisite modules: None. 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 will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will include: The Data Revolution, Exploring Data, Machine Learning & Statistics, Data in Society & Business, Social Networks & Text Analysis.

Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop your skills in programming (in Python), data handling, statistics and visualisation. You will undertake presentations exploring the potential impact of data science in your own organisation and in wider society. Alongside the teaching blocks, you will complete the module through individual study and coursework, supported by the module staff.

Assessment will include assessed practical exercises and coursework.

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. Discuss the roles and impact of data science in industry and society.

2. Demonstrate competence in handling and visualising complex datasets.

3. Describe some of the main topics and techniques used in data science.

Discipline Specific Skills and Knowledge

4. Identify some ethical issues associated with data science in society and business.

5. Use Python to explore data.

6. With some guidance, employ basic data science techniques to explore data.

7. With some guidance, use basic techniques in sub-disciplines of data science, such as machine learning, statistics, network analysis.

Personal and Key Transferable / Employment Skills and Knowledge

8. Communicate ideas and techniques fluently using written means in a manner appropriate to the intended audience.

9. Communicate ideas effectively in oral presentations.

10. Work effectively as part of a team.

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

Topics (with associated exercises and seminar discussions) such as: The Data Revolution

Exploring Data with Python Machine Learning & Statistics Data in Society & Business Social Networks & Text 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	0.00

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	18 hours	1,2,5,6,7	Oral
Feedback in seminar discussions	6 hours	3,4,8,9,10	Oral

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Written essay and programming practice	60	(e.g.) ~3000-word report/program	All	Written
Written essay and programming practice	40	(e.g.) ~15-min presentation	All	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
Coursework	Report and presentation	All	Before next academic year

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	Publisher	Year	Search
Set	Downey, A.B.	Think Python	Green Tea Press/O'Reilly	2015	[Library]
Set	Schutt, R. and O’Neill, C.	Doing Data Science: Straight Talk from the Frontline	O'Reilly	2014	[Library]
Set	Mayer-Schonberger V. & Cukier K.	Big data: a revolution that will transform how we live work and	John Murray	2013	[Library]
Set	Downey, A.B.	Think Stats.	O'Reilly Media.	2014	[Library]

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

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, statistics, data governance, data visualisation, data exploration, social networks, text analysis

Computer Science

COMM414DA - Introduction to Data Science (Professional) (2023)