7CS512 Trade Analytics Coursework 2 Evaluation Brief 2026 | University of Derby
7CS512 Trade Analytics CW2 Evaluation Brief
Description of the Evaluation
This coursework is an particular particular person evaluation that requires you to rep and put in drive a full alternate analytics resolution the train of a sincere-world dataset.
You can rep all data preparation and analytics the train of Python, followed by the introduction of a dashboard the train of either Energy BI or Tableau. The aim is to point to your ability to generate analytical insights the train of a form of tools and to keep up a correspondence these insights effectively.
The evaluation displays the chubby analytics lifecycle coated within the module, from data acquisition and preparation to evaluation, visualisation, and interpretation of outcomes.
Evaluation Declare
You are required to rep and put in drive a alternate analytics resolution for a sincere-world dataset.
Dataset Desire
It be well-known to determine a sincere-world dataset that is upright for alternate analytics.
Your chosen dataset must be permitted by the module leader before you birth up your evaluation.
Project Deliverables and Required Building
The next sections define the anticipated substances of your submission and book how your work must be structured.
1. Command Context and Dreams
- Description of the alternate context
- Definition of the venture or seek data from being addressed
- Sure analytical objectives
2. Recordsdata Acquisition and Preparation (Python)
- Description of the dataset and data provide
- Recordsdata quality considerations and preparation steps
- Recordsdata cleansing, transformation, and structuring the train of Python
3. Analytics Tactics and Modelling (Python)
- Utility of appropriate statistical and machine studying ways
- Justification for the chosen solutions
- Explanation of how the ways tackle the analytical objectives
4. Visualisation and Dashboard Building
- Visualisation of outcomes generated by statistical and machine studying evaluation in Python
- Building of a dashboard the train of Energy BI or Tableau
- Sure presentation of analytical findings
5. Well-known Reflection
- Evaluation of the everyday and limitations of the analytics resolution
- Dialogue of assumptions, constraints, and capability enhancements
- Ethical, lawful, or governance concerns where linked
Technical Instrument Necessities
All data preparation, evaluation, and modelling must be utilized the train of Python.
The evaluation must be implemented and introduced in a Jupyter Notebook, which is the compulsory structure for this evaluation.
Besides to, you prefer to get a dashboard the train of either Energy BI or Tableau essentially essentially based fully on the outcomes generated in Python.
The notebook must be:
- Clearly structured
- Neatly commented where appropriate
- Reproducible
- Easy to coach for a third event
Demonstration Necessities
You are required to present a demonstration of your analytics resolution.
The demonstration schedule will likely be finalised in some unspecified time in the future of Weeks 10 and 11.
The demonstration will happen after the closing submission.
Evaluation Rubric
| Component | Criterion | Fine (70–100%) | Merit (60–69%) | Merely (50–59%) | Nearly (40–49%) | Unsatisfactory (0–39%) |
|---|---|---|---|---|---|---|
| Python | Command context and analytical objectives (5) | Sure and smartly-defined alternate context with centered and linked analytical objectives. For 80% and above: objectives point to accurate analytical focal point and strategic relevance. | Sure context and objectives, although focal point would be uneven. | Context and objectives identified but lack readability or focal point. | Restricted or unclear context and objectives. | No clear alternate context or objectives. |
| Python | Recordsdata acquisition and preparation (15) | Recordsdata is precisely sourced, totally cleaned, remodeled, and smartly prepared the train of Python. For 80% and above: data preparation is powerful, efficient, and smartly justified. | Recordsdata preparation is suitable with minor considerations or restricted justification. | Traditional data preparation done but lacks depth or consistency. | Restricted or aged data preparation. | Recordsdata preparation is missing or unsuitable. |
| Python | Analytics ways and modelling (15) | Appropriate statistical and machine studying ways are precisely implemented and justified. For 80% and above: ways are utilized rigorously with accurate analytical judgement. | Appropriate ways utilized with more inexpensive justification. | Tactics utilized but with restricted justification or technical considerations. | Restricted or detrimental ways venerable. | Tactics are missing or incorrectly utilized. |
| Python | Interpretation of analytical outcomes (15) | Outcomes are clearly interpreted and linked to the analytical objectives. For 80% and above: interpretation demonstrates clear analytical reasoning. | Outcomes are interpreted with more inexpensive readability. | Traditional interpretation supplied, but lacks depth. | Restricted or unclear interpretation. | No meaningful interpretation supplied. |
| Python | Code quality, structure, and reproducibility (5) | Code is smartly structured, readable, and reproducible inner a Jupyter Notebook. For 80% and above: code demonstrates authentic quality and superb observe. | Code is commonly clear and reproducible with minor considerations. | Code runs, however the structure or readability is inconsistent. | The code is poorly structured or now not easy to coach. | Code is incomplete, non-functional, or missing. |
| Dashboard | Dashboard rep and analytical alignment (20) | Dashboard clearly displays outcomes generated in Python and communicates insights effectively. For 80% and above: dashboard rep demonstrates unbelievable analytical alignment and data conversation observe. | Dashboard displays analytical outcomes with more inexpensive readability. | Dashboard is contemporary but weakly linked to evaluation. | The dashboard is proscribed or poorly aligned with the evaluation. | Dashboard is missing or now not essentially essentially based fully on evaluation. |
| Demonstration | Explanation, insight, and figuring out (25) | Demonstration clearly explains the analytics resolution, dashboard, and insights with confidence. For 80% and above: explanation reveals deep figuring out, serious insight, and whole possession of the work. | Sure explanation of the resolution with an correct figuring out. | Ample explanation but restricted depth or readability. | Broken-down explanation with gaps in figuring out. | Unable to showcase or account for the work introduced. |
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