Introduction to Data Science (Professional) - 2023 entry
| 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 | Guided Independent Study | 110 | Placement / Study Abroad | 0 |
|---|
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
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 |
|---|---|---|---|---|---|---|
| Set | Downey, A.B. | Think Python | Green Tea Press/O'Reilly | 2015 | ||
| Set | Schutt, R. and O’Neill, C. | Doing Data Science: Straight Talk from the Frontline | O'Reilly | 2014 | ||
| Set | Mayer-Schonberger V. & Cukier K. | Big data: a revolution that will transform how we live work and | John Murray | 2013 | ||
| Set | Downey, A.B. | Think Stats. | O'Reilly Media. | 2014 |
| 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 6th August 2019 | LAST REVISION DATE | Tuesday 24th January 2023 |
| KEY WORDS SEARCH | data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis |
|---|
Please note that all modules are subject to change, please get in touch if you have any questions about this module.