Prerequisites for Module
None except for course entry requirements.
Corequisite Modules
None.
Precluded Modules
None.
Aims of Module
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
On completion of this module, students are expected to be able to:
| 1. |
Critically appraise data transformation methods for statistical analysis.
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| 2. |
Justify analysis methods and conclusions by selective and critical use of relevant theories.
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| 3. |
Design, implement and evaluate the data analytic lifecycle stages: clean, transform, analyse and visualise.
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| 4. |
Communicate conclusions, insights and recommendations to a wider audience by tailoring them at different levels of detail.
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Indicative Module Content
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).
Indicative Student Workload
Contact Hours
| Part Time | Laboratories
| 24 | Lectures
| 24 |
Directed Study
| | Coursework Preparation
| 25 | Directed Reading
| 35 |
Private Study
| | Private Study
| 42 |
Mode of Delivery
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.
Assessment Plan
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Learning Outcomes Assessed
| | Component 1 | 1,2,3,4
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Component 1 - Data analysis project, 15% in class presentation, 85% report including results and data analysis code.
Indicative Bibliography
| 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.
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