Working with Data - 2019 entry
MODULE TITLE | Working with Data | CREDIT VALUE | 15 |
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MODULE CODE | MTHM501 | MODULE CONVENER | Prof Mark Kelson (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 0 | 0 |
Number of Students Taking Module (anticipated) | 15 |
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The ability to extract information from data as a basis for evidence-based decision is becoming increasing important across a wide variety of sectors in the world of big data, including industry, finance, health, and the environment. This module will equip you with the tools required to collate, import and manipulate data together with methods for basic inference. You will be introduced to different types and sources of data and the tools for performing initial data analysis including producing simple graphical summaries of data and more sophisticated methods for visualising structures in data. These techniques are crucial both as the basis for communication and informing more complex modelling.
Pre-requisites: None
The aim of this module is to equip students with the skills they will need to manipulate, analyse and interpret data appropriately. There will be an introduction to the technical skills that are required to import data from various sources and to format it in a way that allows efficient analysis. An important part of this will be the ability to merge information from multiple sources in order to answer questions and gain extra insight. The module will introduce techniques for performing data cleansing and knowledge discovery, ranging from simple summary statistics to sophisticated graphical representation of patterns in data. Learning these skills will be based on a combination of taught material and ‘hands-on’ sessions using R/RStudio. In addition to the technical aspects of working with data, the module will cover issues that affect the way we use data to inform decision-making; these include issues associated with data collection, bias, uncertainty, and missing data. The aim is that students have an appreciation of these concepts, and ways in which their effects might be mitigated, and are able to communicate possible issues with the analysis of data when writing reports and making recommendations based on statistical analyses.
Activities will include data wrangling, data analysis and report writing and presentation. Assessment will be based on a series of practical examples using real-world data examples that aim to demonstrate the full range of skills require to make effective use of data.
On successful completion of this module, you should be able to:
Module Specific Skills and Knowledge:
1 Demonstrate an understanding of how the source of data, and how it was collected, can have an effect on subsequent analyses;
2 Demonstrate the ability to import, manipulate and summarise data, including an understanding of the relative merits of different methods of formatting;
3 Use R/RStudio effectively in order to facilitate data wrangling and initial data analyses;
Discipline Specific Skills and Knowledge:
4 Demonstrate an understanding of data analyses;
5 Demonstrate an understanding of the important and practical use of the graphical representation of summaries of, and patterns in, data;
6 Understand some common pitfalls in data analysis and how to avoid them;
Personal and Key Transferable/ Employment Skills and Knowledge:
7 Data analysis skills;
8 Use of R/RStudio and other software;
9 Effective use of learning resources;
10 Report writing and presentation.
Topics will include:
• Sources of data;
• Types of data;
• Importing and manipulating data;
• Visualising data;
• Data formats;
• Sources of bias and uncertainty;
• Missing data;
• Knowledge discovery;
• Graphical representation of data;
• Summarising data;
• Introduction to the statistical analyses of data;
• Using data for evidence-based decision-making;
• Communicating results.
Scheduled Learning & Teaching Activities | 36 | Guided Independent Study | 48 | Placement / Study Abroad | 0 |
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Category | Hours of study time | Description |
Scheduled Learning and Teaching Activities | 12 | Lectures |
Scheduled Learning and Teaching Activities | 24 | Hands-on practical sessions |
Guided Independent Study | 30 | Background reading |
Guided Independent Study | 84 | Assessed data analyses, report writing and preparation for presentations |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Feedback on unassessed data analyses examples (which will include report writing) | 24 | All | Oral and Written |
Coursework | 50 | Written Exams | 0 | Practical Exams | 50 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Assessed data analyses and reports from practical sessions (selected ones from the weekly sessions) | 50 | 1.5 hours x 4 | All | Oral and Written |
Coursework – extended piece of data analysis involving data collection, analysis and reporting | 50 | Max. 10 pages (plus appendices) | All | Oral and Written |
As above
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 re-assessment 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%.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic Reading:
R for Data Science - Garrett Grolemund and Hadley Wickham
Statistics, A very short Introduction - David Hand
The art of statistics: Learning from Data - David Spiegelhalter
Discovering statistics using R – Andy Field, Jeremy Miles, and Zoe Field
R programming for Data Science – Roger Peng
Web Based and Electronic Resources:
Install Rstudio
Explore the exciting world of visualisation by looking at the R Graph Gallery - 'Art from Data'
Sign up to simplystatistics.org and blog.revolutionanalytics.com
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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Set | Grolemund, G. and Wickham, H. | R for Data Science | O'Reilly Media | 2016 | 978-1491910399 | |
Set | Hand, D.J. | Statistics: A Very Short Introduction | 1st | Oxford University Press | 2008 | 978-0199233564 |
Set | Spiegelhalter, D. | The Art of Statistics: Learning from Data | Pelican | 2019 | 978-0241398630 | |
Set | Field, A., Miles, J. and Field, Z. | Discovering Statistics Using R | 1st | SAGE Publications Ltd | 2012 | 978-1446200469 |
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 | Monday 17th June 2019 | LAST REVISION DATE | Friday 13th September 2019 |
KEY WORDS SEARCH | None Defined |
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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.