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Talk to an Expert| Category | Assignment | Subject | Business |
|---|---|---|---|
| University | Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) | Module Title | BTD3353 Data Analysis And Visualization For Engineers |
Deadline : 3 July 2026
CLO3: Demonstrate data analysis technique and solution to designated applied science, technology and/or engineering problem using statistic analysis tool and data visualization in Excel/R/Python/Power BI.
Download any relevant (applied sciences/engineering/technology) dataset from open sources website to produce a Power BI dashboard to illustrate the overall data and trend analysis with data modelling in the applied sciences/engineering field.
Use 5Ws and 1 H essential questions. Who, What, Where, When, Why and How to produce a comprehensive dashboard that tells the story of the chosen data. Do all data transformation whenever necessary and set clear objectives in analysing the dataset. Be creative and utilise all the tools provided in the Power BI and Power Query.
Submission → share the link to your published Power BI dashboard using app.powerbi.com
| Content (5%) | (1) Very Weak | (2) Weak | (3) Fair | (4) Good | (5) Very good |
|---|---|---|---|---|---|
| Contain at least 1 significant value/parameter, not a standalone or is not well-described dashboard, incorrect data formatting, many incorrect units, and many incorrect words of technical terms | Contain at least 2 significant value/parameters, not a stand-alone or is not well-described dashboard, few incorrect data formatting, few incorrect units, and few incorrect words of technical terms | Contain at least 3 significant value/parameters, a stand-alone or well-described dashboard, few incorrect data formatting, few incorrect units, and few incorrect words of technical terms | Contain at more than 3 significant value/parameters, a stand-alone or well-described dashboard, few incorrect data formatting, few incorrect units, and correct words of technical terms | Contain all parameters given in the dataset, a stand-alone or well-described dashboard, correct data formatting, correct units, and correct words of technical terms | |
| Design (5%) | (1) Very Weak | (2) Weak | (3) Fair | (4) Good | (5) Very good |
| Many incorrect chart types, non-interactive (not a real-time dashboard), no slicer, no-tile, legend or parameters, labelled to the parameters, dashboard | Many incorrect chart types, interactive (a real-time), slicer, no-tile, legend or parameters, readable, unreadable, dashboard | Few incorrect (1-2) chart types, interactive (a real-time), slicer, has title, legend and labelled to the parameters, readable, dashboard is in monotone colors | All chart types are almost correct (allow 1 incorrect), interactive (a real-time), slicer, has title, legend and labelled to the parameters, readable, labelled to the parameters, | All chart types must correct, interactive (a real-time), slicer, has title, legend and labelled to the parameters, readable, dashboard is colorful and fully filled/utilized. |
| Criteria | (1) Very Weak | (2) Weak | (3) Fair | (4) Good | (5) Very Good |
|---|---|---|---|---|---|
| Dataset Relevance (%) | Irrelevant/Toy Data: | Basic/Generic: | Acceptable: | Technical: | High-Fidelity Engineering: |
| Dataset is fictional, unrelated to engineering (e.g., retail sales), or has insufficient variables (only 1-2 columns). | Generic public dataset (e.g., “Car Sales” instead of “Engine Performance”). | Relevant topic (e.g., “Global Temperature”), but limited depth. | Real-world engineering context (e.g., “Sensor logs,” “Tensile strength tests”). | Complex, authentic source (e.g., NIST, NASA, experimental lab data). | |
| No Context: No units or physical meaning defined. | Low Complexity: Data is purely categorical; lacks continuous variables needed for scientific analysis (e.g., time-series or sensor readings). | Standard Units: Units are present but simple. | Multi-variate: Includes multiple continuous variables (Temp, Pressure, Velocity) allowing for correlation. | Time-Series/Sensor Data: Handles high-frequency or granular data effectively. | |
| Descriptive: Shows what happened, but lacks data to explain how (physics/logic). | Derived Metrics: Includes basic calculated engineering fields. | Physics-Based: Data allows for derivation of complex metrics (e.g., calculating Efficiency from Input/Output power). |
| Criteria | (1) Very Weak | (2) Weak | (3) Fair | (4) Good | (5) Very Good |
|---|---|---|---|---|---|
| Data Modelling (5%) | Unstructured: | Poor Structure: | Functional: | Structured: | Optimized: |
| All data in a single flat table (no model) OR relationships are missing/broken. | Many-to-many relationships used incorrectly; cross-filter direction causes ambiguity. | Relationships work but schema is messy (e.g., mixing fact/dimensions). | Clear Star Schema (Fact vs. Dimension tables). | Perfect Star Schema; 1-to-many relationships only. | |
| No ETL: Raw data loaded directly with errors/null. | Minimal ETL: Basic cleaning, but confusing column names remain. | Adequate ETL: Correct data types applied; major errors removed. | Good ETL: Columns renamed for readability; date table created. | Pro ETL: Unnecessary columns removed; hierarchies defined; query steps efficient. | |
| No DAX: No calculations; relies only on implicit sums. | Basic DAX: Only calculated columns used (no measures). | Standard DAX: Basic measures created (Sum, Count) and work correctly. | Intermediate DAX: Usage of CALCULATE or Time Intelligence (YTD/MoM) functions. | Advanced DAX: Complex logic handled via measures; measures organized in folders; correct formatting (%, $). |
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