Introduction to Data Science - 2019 entry
MODULE TITLE | Introduction to Data Science | CREDIT VALUE | 15 |
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MODULE CODE | ECMM443 | MODULE CONVENER | Prof Hywel Williams (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 |
Number of Students Taking Module (anticipated) | 30 |
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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, machine vision and high-performance computing. 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.
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, Machine Vision, Information Security. 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 and/or R), 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.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
2. Demonstrate competence in handling, exploring and visualising complex datasets.
Discipline Specific Skills and Knowledge
5. Use Python and/or R languages to explore data.
Personal and Key Transferable / Employment Skills and Knowledge
9. Communicate ideas effectively in oral presentations.
Example topics (with associated exercises and seminar discussions):
The Data Revolution
Exploring Data with R and/or Python
Machine Learning and Statistics
Data in Society and Business
Social Networks and Text Analysis
High Performance Computing and Data Architectures
Machine Vision
Information Security
Scheduled Learning & Teaching Activities | 36 | Guided Independent Study | 114 | Placement / Study Abroad | 0 |
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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 |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Feedback on practical work | 18 hours | All | Oral |
Feedback in seminar discussions | 6 hours | All | Oral |
Coursework | 70 | Written Exams | 0 | Practical Exams | 30 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Coursework 1 | 40 | 2000 words, plus presentation | 1,3,4,8,9,10 | Written |
Coursework 2 | 30 | Report, plus code | 2,4,5,6,7,8, | Written |
Assessed practical | 30 | 1 hour | 2,5,6,7 | Oral |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Coursework 1 | Report, plus presentation | 1,3,4,8,9,10 | Summer reassessment period with an August deadline |
Coursework 2 | Report, plus code | 2,4,5,6,7,8 | Summer reassessment period with an August deadline |
Assessment practical | 1 hour | 2,4,5,6,7 | Summer reassessment period with an August deadline |
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 has been taken as a result of referral will be capped at 50%.
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 |
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Set | Downey, A.B. | Think Stats. | O'Reilly Media. | 2014 | ||
Set | Grolemund, G. and Wickham, H. | R for Data Science | O'Reilly Media | 2016 | 978-1491910399 | |
Set | Mayer-Schonberger V. & Cukier K. | Big data: a revolution that will transform how we live work and | John Murray | 2013 | ||
Set | Marr, B. | Big Data in Practice | Wiley | 2016 | ||
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 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Tuesday 10th July 2018 | LAST REVISION DATE | Tuesday 10th July 2018 |
KEY WORDS SEARCH | data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis, machine vision |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.