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Engineering, Mathematics and Physical Sciences Intranet
ECMM443 - Introduction to Data Science (2023)
| MODULE TITLE | Introduction to Data Science | CREDIT VALUE | 15 |
|---|---|---|---|
| MODULE CODE | ECMM443 | MODULE CONVENER | Dr Rudy Arthur (Coordinator) |
| DURATION: TERM | 1 | 2 | 3 |
|---|---|---|---|
| DURATION: WEEKS | 11 |
| Number of Students Taking Module (anticipated) | 30 |
|---|
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 and visualisation, machine learning, statistical modelling, social network analysis, text mining. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.
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 evolve to reflect current issues in data science, but are likely to include: The Data Revolution, Exploring Data, Machine Learning & Statistics, Data in Society & Business, Social Networks & Text Analysis, High Performance Computing & Data Architectures. The module will also cover ethics and governance around data science.
Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation. You will undertake discussions and group presentations to explore aspects of data science and its impacts on society.
Assessment will include assessed practical exercises, presentations, and an investigation of a chosen aspect of data science.
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, exploring 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, machine vision and high-performance computing.
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
Example topics (with associated exercises and seminar discussions):
The Data Revolution
Exploring Data with Python
Machine Learning and Statistics
Data in Society and Business
Social Networks and Text Analysis
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
| Scheduled Learning & Teaching Activities | 36.00 | Guided Independent Study | 114.00 | Placement / Study Abroad | 0.00 |
|---|
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
| Category | Hours of study time | Description |
| Scheduled Learning and Teaching | 36 | Lectures, Practicals, Seminars |
| Guided Independent Study | 51 | Coursework |
| Guided Independent Study | 63 | Self-study and Background Reading |
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 | All | Oral |
| Feedback in seminar discussions | 6 hours | All | Oral |
SUMMATIVE ASSESSMENT (% of credit)
| Coursework | 60 | Written Exams | 0 | Practical Exams | 40 |
|---|
DETAILS OF SUMMATIVE ASSESSMENT
| Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
|---|---|---|---|---|
| Coursework | 60 | Report and presentation | 1,2,3,4,5,6,7,8,9,10 | Written |
| Class Test | 40 | 1 hour | 2,4,5,6,7,8 | 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 | 1,2,3,4,5,6,7,8,9,10 | Summer reassessment period with an August deadline |
| Class test | Class test, 1 hour | 2,4,5,6,7,8 | Summer reassessment period with an August deadline |
RE-ASSESSMENT NOTES
Reassessment will be by coursework and/or test in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
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 | Search |
|---|---|---|---|---|---|---|---|
| Set | Downey, A.B. | Think Stats. | O'Reilly Media. | 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 | Marr, B. | Big Data in Practice | Wiley | 2016 | [Library] | ||
| 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] |
| 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 10 July 2018 | LAST REVISION DATE | Wednesday 18 January 2023 |
| KEY WORDS SEARCH | data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis |
|---|