Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Introduction To Data Analytics And Visualisation | |||
Reference | CB2012 | Version | 2 |
Created | January 2020 | SCQF Level | SCQF 8 |
Approved | October 2018 | SCQF Points | 30 |
Amended | June 2020 | ECTS Points | 15 |
Aims of Module | |||
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To enable students to apply the principles of Data Analytics and Visualisation to inform business processes and decisions. |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Evaluate different data analysis techniques in response to a business problem |
2 | Appraise different types of data visualisation and the contexts within which they may be applied |
3 | Prepare and manage data sets and sources for data visualisation |
4 | Apply data visualisation tools and techniques to explore, analyse and present data |
Indicative Module Content |
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Understanding the data analytics and data mining lifecycle (CRISP-DM); data driven strategy. A broad overview of key analytics concepts and principles including: descriptive analytics; predictive analytics; classification models. Principles of data visualisation; data preparation and evaluation; data representation; chart types; data-driven storytelling; visual analytics; dashboard design; ethics of visualisation. |
Module Delivery |
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The module is delivered via online exercises, workshops, case studies and lab tutorials. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | 48 | N/A |
Non-Contact Hours | 252 | N/A |
Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
TOTAL | 300 | N/A |
Actual Placement hours for professional, statutory or regulatory body |   |   |
ASSESSMENT PLAN | |||||
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If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
Component 1 | |||||
Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
Description: | Individual Portfolio Assessment |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module. | |
Module Grade | Minimum Requirements to achieve Module Grade: |
A | The student needs to achieve an A in C1. |
B | The student needs to achieve a B in C1. |
C | The student needs to achieve a C in C1. |
D | The student needs to achieve a D in C1. |
E | The student needs to achieve an E in C1. |
F | The student needs to achieve an F in C1. |
NS | Non-submission of work by published deadline or non-attendance for examination |
Module Requirements | |
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Prerequisites for Module | None. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | ACHARYA, S. and CHELLAPPAN, S. (2017). Pro Tableau. New York: Apress |
2 | FEW, S. (2012). Show Me The Numbers. Burlingame, CA: Analytics Press |
3 | FOREMAN, J. (2013). Data Smart: Using Data Science to Transform Information Into Insight. Indianapolis: Wiley |
4 | KNAFLIC, C. (2015). Storytelling with data. New Jersey: Wiley |
5 | MAYER-SCHONBERGER, V. and CUKIER, K. (2013). Big data. A Revolution that will transform how we live, work and think. London: John Murray |
6 | MURRAY, D. (2016). Tableau your data!. Indianapolis: Wiley |
7 | PROVOST, F. and FAWCETT, T. (2013). Data science for business. Sebastopol, CA: O'Reilly Media |