| Week | Dates | Topic / Content | Assessment / Notes |
|---|---|---|---|
| 1 | 28 Sep – 4 Oct 2026 | Chapter 1: Introduction — exploratory vs explanatory analysis; communicating with data. | |
| 2 | 5 – 11 Oct 2026 | Chapter 2: Effective visual & reporting — tables, figures and text. | |
| 3 | 12 – 18 Oct 2026 | Chapter 2: Visual types (table, points, lines); ends with Gantt chart. | |
| 4 | 19 – 25 Oct 2026 | Chapter 2: Visual types (bars, histogram, boxplot, geospatial); cautious elements. | |
| 5 | 26 Oct – 1 Nov 2026 | Chapter 4: Google Sheets — intro & simple plots (Vol 1). Case-study report briefed. | |
| 6 | 2 – 8 Nov 2026 | Chapter 4: Simple plots using Google Sheets (Vol 2). | |
| 7 | 9 – 15 Nov 2026 | Chapter 4: Infographics dashboard using Google Sheets (Vol 3). | |
| 8 | 16 – 22 Nov 2026 | Chapter 3: Storytelling — storyboarding, report & narrative structure. | Lab Test (20%) — Ch 1, 2 & Google Sheets |
| 9 | 23 – 29 Nov 2026 | Chapter 4: Introduction to Power BI (Vol 1). Case-study presentation briefed. | |
| 10 | 30 Nov – 6 Dec 2026 | Chapter 4: Simple plots using Power BI (Vol 2). | |
| 11 | 7 – 13 Dec 2026 | Chapter 4: Infographics dashboard using Power BI (Vol 3). | |
| 12 | 14 – 20 Dec 2026 | Case-study project: supervision, data collection & group discussion. | |
| — | 21 – 27 Dec 2026 | Mid-semester break — no classes. | |
| 13 | 28 Dec 2026 – 3 Jan 2027 | Final preparation for case-study report & presentation. | Final Test (30%) — Ch 2 & Power BI (practical) |
| 14 | 4 – 10 Jan 2027 | Chapter 5: Case study — project presentation & report submission. | Project report (25%) + Presentation (25%) |
| — | Study week: 11 Jan 2027 | Study week & final assessment period. | Final exams from 18 Jan 2027 |
Assessment summary
Lab Test
Ch 1–2 & Google Sheets
Week 8Group Project
Case-study report
Week 14Full brief & rubric →Presentation
Present your case study
Week 14Full brief & rubric →Final Test
Practical: Ch 2 & Power BI
Week 1370% continuous assessment · 30% practical final test · Passing mark 50% for each component. The lecturer may adjust the schedule as needed; you will be informed of any changes.
Academic integrity
Everything you submit for STA191 must be your group's own work. This matters more in a data course than most: a chart is a claim about reality, and a dishonest chart misleads everyone who reads it.
- Using open data from DOSM, data.gov.my or UiTM — with the source cited.
- Learning a technique from a book, video or website, then applying it to your own data.
- Quoting or paraphrasing a source in your introduction, cited in APA 7th.
- Discussing ideas with another group, then doing your own analysis and writing.
- Using AI tools to check grammar or explain a concept — if you disclose it and the analysis, charts and words are yours.
- Copying text from a book, website or another group's report without citing it.
- Fabricating or altering data — inventing questionnaire responses, or editing values to make a chart look better.
- Submitting a report or dashboard produced by someone outside your group, including one generated wholesale by AI.
- Reusing a report submitted for another course or an earlier semester.
- Putting a group member's name on work they did not contribute to.
A chart can mislead without a single false number — a truncated axis, a cherry-picked date range, a 3-D pie. Chapter 2 teaches you to spot these. Doing it deliberately in your own report is treated as misconduct, not a design choice.
Consequences. Suspected misconduct is referred under UiTM's academic regulations and may result in a zero for the component, failure of the course, or further disciplinary action. If you are unsure whether something is allowed, ask before you submit — asking is always safe.