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| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Analysis | |||
| Reference | CMM703 | Version | 2 |
| Created | October 2017 | SCQF Level | SCQF 11 |
| Approved | May 2016 | SCQF Points | 15 |
| Amended | November 2017 | ECTS Points | 7.5 |
| 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. |
| 2 | Justify analysis methods and conclusions by selective and critical use of relevant theories. |
| 3 | Design, implement and evaluate the data analytic lifecycle stages: clean, transform, analyse and visualise. |
| 4 | Communicate conclusions, insights and recommendations to a wider audience by tailoring them at different levels of detail. |
| 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). |
| Module 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. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| 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 | |||||
|---|---|---|---|---|---|
| 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, 15% in class presentation, 85% report including results and data analysis code. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| 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 | |
|---|---|
| Prerequisites for Module | None except for course entry requirements. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| 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. |