Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Data Visualisation | |||
| Reference | CM4125 | Version | 5 |
| Created | September 2023 | SCQF Level | SCQF 10 |
| Approved | May 2020 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce, appraise and interpret data visualisation techniques, and understand the challenges associated with visualising large datasets. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critique a variety of data visualisation techniques in terms of psychology, design, effectiveness and appeal to a wider audience. |
| 2 | Question data visualisations and explain what conclusions can be drawn from them in terms of data analysis. |
| 3 | Illustrate an understanding of the challenges of visualising large datasets. |
| 4 | Invent a coherent pictorial representation of numerical and categorical data. |
| 5 | Compose novel and interactive data visualisations which lucidly exhibit particular dataset features. |
| Indicative Module Content |
|---|
| 1. Introduction to data visualisation 2. Tools for data visualisation 3. Data loading and preprocessing 4. Data cleaning 5. Designing graphs 6. Understanding and creating infographics 7. Visualising multiple variables 8. Data Dashboards |
| Module Delivery |
|---|
| Key concepts are introduced and illustrated through lectures, workshops and directed reading. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | N/A |
| 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, 5 |
| Description: | A report describing how the student collected, analysed and processed a group of data sets, plus the creation of a data visualisation resource (i.e. a dashboard or an infographic). | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| 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 | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | McCandless, D. 2014. Knowledge is Beautiful. 1st Ed. Williams Collins. |
| 2 | Cairo, A. 2016. The Truthful Art. 1st Ed. Pearson. |
| 3 | McCandless, D. 2013. Information is Beautiful. 1st Ed. Collins. |
| 4 | Microsoft Power BI Community https://community.powerbi.com/t5/Themes-Gallery/bd-p/ThemesGallery |
| 5 | Korchia, N, and Voignier, F. From Hadoop to the Cloud: How to simply Modernize Analytics Applications? https://indexima.com/portfolio/webinar-oneclick-aws/? cid=5c7bf3cce317a76bb3191a42&utm_medium=email&utm_campaign =5e9864e4eeec3720896ef66d&utm_source=plezi-smart-campaign |
| 6 | Tableau Certified Associate Exam Guide https://www.udemy.com/course/tableau-certified-associate-exam-guide/ |
| 7 | Power BI A-Z: Hands-On Power BI Training for Data Science! https://www.udemy.com/course/mspowerbi/ |
| 8 | Plotly, Dash App Gallery https://dash-gallery.plotly.host/Portal/ |