Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Data Analysis | |||
Reference | CMM703 | Version | 4 |
Created | February 2024 | SCQF Level | SCQF 11 |
Approved | May 2016 | SCQF Points | 15 |
Amended | April 2024 | ECTS Points | 7.5 |
Aims of Module | |||
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To provide in-depth knowledge of the data analytic lifecycle and specialised programming knowledge through the use of a state-of-the-art data analysis language (such as R). |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Appraise data transformation methods for statistical analysis. |
2 | Deal with relevant theories to justify analysis methods and conclusions for data analytic problems. |
3 | Construct the data analytic lifecycle stages to demonstrate clean, transform, analyse and visualise processes. |
4 | Produce conclusions, insights and recommendations for a wider audience by tailoring them at different levels of detail. |
Indicative Module Content |
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Data preparation: raw data into vectors, handle variables, and perform basic data cleaning functions. Manipulate structures: matrices, lists, factors, and data frames. Probability: distributions, and random variables. Analysis: calculate statistics and confidence intervals, and perform statistical tests. Create a variety of graphic displays. Build statistical models (regressions) and analysis of variance (ANOVA) Explore advanced statistical techniques (cluster and link analysis). |
Module Delivery |
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This is a lecture-based module, supplemented with practical sessions, where a Data Analysis programming language will be used to teach students, the different stages involved with data analysis. For on-campus learners, teaching and learning will be facilitated hands-on at lecture halls and labs. For online learners teaching and learning will be facilitated in real-time via virtual classrooms using voice and video, collaborative tools, and remote assistance tools. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | N/A | 48 |
Non-Contact Hours | N/A | 102 |
Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
TOTAL | N/A | 150 |
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: | Data analysis project consisting of an in class presentation and a report including results and data analysis code. |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
The student must have a grade D on C1 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 except for course entry requirements. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | TEETOR, P., 2011. R cookbook. O'Reilly Media, Inc. |
2 | FIELD, A., MILES, J., FIELD, Z., 2012. Discovering Statistics Using R. SAGE Publications. |
3 | MATLOFF, N., & MATLOFF, N. S., 2011. The art of R programming: a tour of statistical software design. No Starch Press. |
4 | WINSTON, C., 2013. R Graphics Cookbook. O’Reilly. |
5 | ABEDIN, J. and MITTAL, H., 2014. R Graphs Cookbook 2nd Ed. Packt Publishing. |