Data Visualisation Techniques Individual Assignment 2026 | CCT College Dublin
| University | CCT College Dublin (CCT) |
| Subject | Data Visualisation Techniques |
Data Visualisation Techniques Individual Assignment
| Programme Title: | HDIP Data Analytics | ||
| Cohort: | HDipData_Sept25_FT/SB+/HCI | ||
| Module Title(s): | Data Visualisation Techniques | ||
| Assignment Type: | Individual | Weighting(s): | 40% |
| Assignment Title: | CA1_DataViz_HDip | ||
| Lecturer(s): | David McQuaid | ||
| Issue Date: | 03/03/2026 | ||
| Submission Deadline Date: | 25/03/2026 | ||
| Late Submission Penalty: | Late submissions will be accepted up to 5 calendar days after the deadline. All late submissions are subject to a penalty of 10% of the mark awarded.
Submissions received more than 5 calendar days after the deadline above will not be accepted and a mark of 0% will be awarded. |
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| Method of Submission: | Moodle | ||
| Instructions for Submission: | Assessment must be submitted to Moodle before 25/03/2026 11:59pm as a Jupyter Notebook file
The Jupyter Notebook File Must be saved as “YourName_DVizHDip_CA1.ipynb” CA Cover Page as Word Document NO PDF’s! NO PYTHON FILES (.py) |
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| Feedback Method: | Results posted in Moodle gradebook | ||
| Feedback Date: | Approx. 3 weeks after FINAL submission (inc PMC cases) | ||
Learning Outcomes
Please note this is not the assessment task. The task to be completed is detailed on the next page.
This CA will assess student attainment of the following minimum intended learning outcomes:
- Discuss the concepts, techniques and processes underlying data visualisation (Linked to PLO 1)
- Critically evaluate visualisation approaches with respect to their suitability for different problem areas.(Linked to PLO 5)
- Select appropriate data visualisation techniques for a given use case, data characteristics and multiple transmission media.(Linked to PLO 3, PLO 4)
Attainment of the learning outcomes is the minimum requirement to achieve a Pass mark (40%). Higher marks are awarded where there is evidence of achievement beyond this, in accordance with QQI Assessment and Standards, Revised 2013, and summarised in the following table:
| Percentage Range | CCT Performance Description | QQI Description of Attainment | |
| Level 6, 7 & 8 awards | |||
| 90% + | Exceptional | Achievement includes that required for a Pass and in most respects is significantly and consistently beyond this | |
| 80 – 89% | Outstanding | ||
| 70 – 79% | Excellent | ||
| 60 – 69% | Very Good | Achievement includes that required for a Pass and in many respects is significantly beyond this | |
| 50 – 59% | Good | Achievement includes that required for a Pass and in some respects is significantly beyond this | |
| 40 – 49%
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Acceptable | Attains all the minimum intended programme learning outcomes | |
| 35 – 39% | Fail | Nearly (but not quite) attains the relevant minimum intended learning outcomes | |
| 0 – 34% | Fail | Does not attain some or all of the minimum intended learning outcomes | |
Please review the CCT Grade Descriptor available on the module Moodle page for a detailed description of the standard of work required for each grade band.
The grading system in CCT is the QQI percentage grading system and is in common use in higher education institutions in Ireland. The pass mark and thresholds for different grade bands may be different from what you have experience of in the higher education system in other countries. CCT grades must be considered in the context of the grading system in Irish higher education and not assumed to represent the same standard the percentage grade reflects when awarded in an international context.
CA1 NOTE DO NOT ZIP YOUR SUBMISSION FILES, ALL FILES MUST BE SUBMITTED INDIVIDUALLY
Use of Artificial Intelligence
Use of Artificial Intelligence is not permitted in this assignment because this assignment is designed to assess students’ independent research, critical thinking, and technical skills in the areas of Data Visualization. The use of AI tools to generate text or code could compromise the authenticity and originality of the work, and introduces the high risk of:
- Misinterpretation of research papers
- Incorrect code or logic
- Inaccurate conclusions
Using AI blindly could introduce errors into your submission, which may affect your grade.
By completing the assignment without AI assistance, students develop essential skills in literature review, critical evaluation, and practical implementation, while ensuring academic integrity and fairness. This approach also encourages creative thinking and personal insight, which are key learning outcomes of this course.
Please Note Students may have to perform an Individual Q&A Session Regarding their Submission, failure to attend will result in a 0 Grade
Note: It is important when using these AI tools to consider data protection, privacy, and copyright. As GenAI tools may harvest text/data or images that are used as inputs (or as part of a prompt), you should first consider the provenance of your inputs and ensure that it is ethical and responsible to upload this information. See the following links for guidance from the EU on data protection and from UNESCO on using AI in research and education (section 2.3, Page 15 is relevant).
EU Data regulation
- https://commission.europa.eu/law/law-topic/data-protection/eu-data-protection-rules_en
- https://www.edps.europa.eu/data-protection/our-role-supervisor/first-edps-orientations-euis-using-generative-ai_en
UNESCO https://school-education.ec.europa.eu/en/discover/publications/guidance-generative-ai-education-and-research
Note ALL Students are required to use Git Classroom for any Assignments that they are working on. This assignments Git Classroom link is: https://classroom.github.com/a/Sw8JaGSA
This means that ALL changes must be committed to the assignments Git classroom during your assignment. (Not just a single commit at the end!) This is to allow you to display your incremental progress throughout the assessments, allows you to create an online portfolio that can be used to showcase your work and to ensure that there are no problems with final uploads (as all your work will be available on GitHub). It is expected that there will be a minimum of 10 commits (with many of you making very many more).
You may Only use your CCT email for your git account, private/work email-based accounts will not be accepted. You DO NOT NEED TO include your lecturer’s CCT email as a collaborator on your submission as they have automatic access.
NOTE As well as committing to the Git Classroom you must also upload your work to Moodle as usual for grading. Failure to do so will result in a 0 Grade.
Assessment Task
Students are advised to review and adhere to the submission requirements documented after the assessment task.
Minimum Requirements
Scenario
You have been retained by a retail company to analyse a dataset based on video games. This analysis will help determine the sales strategy for the company in their upcoming Summer season.
Each answer MUST have ONE separate and different visualization type that can be easily understood, visually represents the answer, and all data wrangling, analysis, and visualizations must be generated using python, in a Jupyter Notebook. Please note that the visualizations MUST BE STATIC, not interactive.
The companies CTO also requires you to rationalize all the decisions that you have made in your Jupyter Notebook report. The CTO has also stipulated that the data is proprietary and cannot be uploaded to any LLM or AI Agent.
This rationalization MUST include your visualization design decisions, how you have engineered the data, feature selection and any other information that you deem relevant.
Requirements
You are required to use the dataset contained within the file “vgchartz-2024.csv” and then answer the following questions:
Part 1: (Column Names are denoted by quotation marks) [0-30]
- What are the top 5 publishers by “other_sales”?
- Is there a correlation between the “pal_sales” and “na_sales” for the “release_date” years 2010-2020?
- What is the distribution of the 3 most popular (by “critic_score”) game genres?
- Do older games (2010 and earlier) have a higher MEDIAN “na_sales” than newer games (after 2010)?
- What are the 5 most common “console” in the dataset?
Part 2 [0-10]
You must answer a “Statistically Relevant” question, OF YOUR OWN CHOOSING, using the dataset, that has not been asked in Part 1. This must have a logical basis that enhances the information and insight gained in the scenario. (ONLY 1!)
Part3: [0-50]
TOTAL WORDS 1000+-10% In Jupyter Markdown NOT a Separate Report
You must explain, in detail,
why you chose the specific methods to engineer the data and how you achieved this in python (Part 1/Part 2)
why you chose your specific visualizations to illustrate each answer (Part 1/Part 2)
what design decisions you made for each visualization (for example, but not only: colour, font, titles, size, text position, font size etc) (Part 1/Part 2)
what your rationale is for the visualization created in Part 2 and how your question enhances the information and insight gained in the scenario (Part 2)
Part 4: [0-10]
Continual commits (at least 10) over the time period of the Assessment 10/03/2026– 12/04/2026. Failure to achieve this will require the student to attend an in-person Viva at CCT.
Note that all written work MUST be completed in Jupyter Notebook Markdown (please review “Jupyter Notebook Tutorial” Notes in Moodle if you are unsure of this), NOT in code comments.
All Code must be included in code blocks (As normal). No other upload will be accepted.
Data Dictionary
Available on Moodle as file “vg_data_dictionary.csv”
Submission Requirements
All assessment submissions must meet the minimum requirements listed below. Failure to do so may have implications for the mark awarded.
All assessment submissions must:
- Use Git Classroom link
- The Jupyter Notebook File Must be saved as “YourName_DPrepHDip_CA1.ipynb”
- Be submitted by the deadline date specified or be subject to late submission penalties
- Be submitted via Moodle upload
- Use Harvard Referencing when citing third party material
- Be the student’s own work.
- Include the CCT assessment cover page.
Additional Information
- Lecturers are not required to review draft assessment submissions. This may be offered at the lecturer’s discretion.
- In accordance with CCT policy, feedback to learners may be provided in written, audio or video format and can be provided as individual learner feedback, small group feedback or whole class feedback.
- Results and feedback will only be issued when assessments have been marked and moderated / reviewed by a second examiner.
- Additional feedback may be provided as individual, small group or whole class feedback. Lecturers are not obliged to respond to email requests for additional feedback where this is not the specified process or to respond to further requests for feedback following the additional feedback.
- Following receipt of feedback, where a student believes there has been an error in the marks or feedback received, they should avail of the recheck and review process and should not attempt to get a revised mark / feedback by directly approaching the lecturer. Lecturers are not authorised to amend published marks outside of the recheck and review process or the Board of Examiners process.
- Students are advised that disagreement with an academic judgement is not grounds for review.
- For additional support with academic writing and referencing students are advised to contact the CCT Library Service or access the CCT Learning Space.
- For additional support with subject matter content students are advised to contact the CCT Student Mentoring Academy
- For additional support with IT subject content, students are advised to access the CCT Support Hub.
Grading Criteria
This grading rubric sets out the marking criteria for your assignment
| Criteria | Five distinct, static visualizations representing answers to five questions. Must include Python-based data wrangling and meaningful feature engineering. | Original question that adds new insight, with valid statistical basis, full analysis, and well-designed static visualization. | Detailed explanation of methods used in data transformation, feature selection, chart types chosen, and visual design decisions. Also includes rationale for Part 2’s added insight. | Demonstrates iterative development via regular Git commits over the assignment period (11/03/2025 – 06/04/2025). |
| Weighting per criteria | 30 marks | 10 marks | 50marks | 10 marks |
| Excellent (+70%) | All five answers are accurate, each uses a unique static visualization with effective design choices (titles, labels, colors, readability), and visualizations clearly support insights derived from engineered data. | Question is unique, clearly motivated, statistically relevant, and adds new insight beyond Part 1; strong Python analysis with a clear, well-designed static visualization. | All techniques are clearly and logically explained including how Python was used to wrangle data, select features, and generate each visualization; every design choice (color, label size, title, layout) is well justified; rationale for Part 2 clearly extends insight. | 10 or more well-distributed, meaningful commits showing consistent work, testing, and improvements. |
| Very Good (60 – 69%) | questions completed to high standard; one may have weak or unclear visualization or slight overlap in chart type. | Good question and visualization, adds some new value, statistical relevance present but less impactful; visualization appropriate. | Explanation covers most areas with clarity; minor gaps in design or methodology detail; Part 2 rationale understandable but less strong. | 8–9 commits with visible progress made over time. |
| Good (50 – 59%) | answers completed with appropriate analysis but may reuse chart types or have weak design; limited explanation of data steps. | Question answered but is basic or not well justified; visualization used but may be unclear or unrelated. | Covers basic methods and some design choices, but explanations are shallow or formulaic; Part 2 rationale weak or generic. | 6–7 commits but clustered or done within a short window. |
| Acceptable (40 – 49%) | poor visualization design; minimal or unclear Python code used for data handling. | Question is weak, duplicates existing analysis, or lacks depth; visual may be present but poorly done or not explained. | Little explanation of how or why methods/visuals were chosen; weak link between data and visual decisions; vague or incomplete. | 3–5 commits with minimal progress visibility; suggests rushed effort. |
| Fail (< 39%) | No meaningful answers; visualizations are missing, interactive, or invalid; code is incorrect or incomplete. | No question posed, or question irrelevant; no meaningful visualization or analysis. | No meaningful rationale; code used without explanation; visualizations unexplained or not relevant to insights. | 0–2 commits or all work committed at once; |
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