CityUHK IS6335: Data Visualization — course overview with AskSia
IS6335 Data Visualization is a 3-credit postgraduate course from the Department of Information Systems at City University of Hong Kong, offered in Semester A 2026/27. It has no prerequisites and the syllabus states that no programming background is required, though coding experience helps.
Sia generates IS6335 practice questions, walks through design theory of visual presentation and tableau step by step, and quizzes you on the material the report that weights most heavily.
Find what is wrong
You want to compare monthly revenue for six product lines over three years. Which chart and encoding does visualization design theory recommend?
Identify the task: comparing trends across categories over time.
Pie charts and 3D columns weaken comparison; a stacked column hides individual product trends.
In ggplot2 this is geom_line() with colour mapped to product; in Tableau a line chart with product on colour.
One report decides 40% of your grade. Continuous. This whole page is built around that.
Overview
What IS6335 is, and where it sits
IS6335 Data Visualization is a 3-credit postgraduate course from the Department of Information Systems at City University of Hong Kong, offered in Semester A 2026/27. It has no prerequisites and the syllabus states that no programming background is required, though coding experience helps.
The course teaches visualization for data interpretation in business: the design theory behind visual reports, GUI-based Tableau, then programmable packages in R and Python, interactive and mobile visualization, and a taste of virtual-reality applications. Weekly time is one hour of lecture and two hours of hands-on tutorial.
Assessment is 100% coursework: class participation 10%, assignments 30%, a group data-story project 20% and a 40% individual project delivered as a written report. A minimum of 50% is required to pass.
Always treat your own course outline and the exam timetable as authoritative.
Difficulty & time commitment
Is IS6335 hard, and how much time does it take?
IS6335 is manageable if you keep a weekly rhythm and treat the back half as the main event. The pattern is consistent: it starts gently and steepens, and the heaviest assessment is the part that separates grades.
The difficulty curve and the assessment weighting point the same way: the back half is harder and worth more. Front-loading effort there is the highest-return decision in the course.
Is this course for you
Who tends to do well, and who tends to struggle
You will likely do well if
- You enjoy arguing why one chart is better than another, not just producing charts.
- You will spend the two tutorial hours actually coding in R and Python.
- You have a dataset you care about for the group story.
You may struggle if
- You expect a coding course; design theory is 40% of the CILO weight.
- You rely on Tableau and never move to the programmable tools that the syllabus weights highest.
- You start the individual report in the last week; it is 40% and judged on design.
- Redraw one published chart each week two ways and write why the second is better.
- Keep an R and a Python notebook of every chart type covered, with the code that makes it.
- Prototype your individual project in Tableau, then rebuild it programmatically to show range.
Syllabus
The 10 topics, topic by topic
Topics from the public source. Their order is a content map, not an official weekly timetable or published exam weighting.
T1 · Design theory of visual presentation
CILO 1How people perceive pictures; choosing the right chart; visual reports in business communication.
T2 · Data charts and dashboards
Keyword syllabusStandard chart types, dashboards and Power View.
T3 · Tableau
CILO 2GUI-based visual analysis and compelling visual reports.
T4 · R visualization packages
CILO 3; compulsory text 1Programmable graphics in R, including ggplot2.
T5 · Python visualization
CILO 3; compulsory text 2Interactive and engaging visuals with Python packages.
T6 · Infographics and text visualization
Keyword syllabusCommunicating with infographics; visualizing text data.
T7 · Social network visualization
Keyword syllabusDrawing and reading network graphs.
T8 · Visualization on mobile devices and VR
Keyword syllabus; abstractDesigning for small screens; virtual-reality applications.
T9 · Visualization for machine learning and statistics
CILO 3Using visuals to augment statistical and ML analysis.
T10 · Building a data-driven story
LTA 3; AT3Identifying a data source and forming an analytics narrative for the group project.
How it's assessed
Assessment structure
| Component | Weight | Format & timing |
|---|---|---|
| Class participation | 10% | Discussion and reflection on lecture and tutorial material. Throughout. Continuous. |
| Assignments (applying the tools) | 30% | Assignments applying the visualization tools (Tableau, R, Python). Across the semester. Continuous. |
| Group project: data-driven story / analytics report from a self-chosen data source | 20% | Group analytics report: identify a data source and build a data-driven story with the tools. Late semester. Continuous. |
| Individual project: written report with innovative visualization designs | 40% | Individual written report applying visualization concepts and designs innovatively. End of semester. Continuous. |
- 100% continuous assessment with a minimum continuous-assessment passing requirement of 50%. GenAI use is allowed on all tasks per the syllabus.
- No examination. The 40% individual project is a written report judged on understanding of visualization design principles and the skills for innovative designs; the 20% group project is judged on design theory and on using both menu-based and programmable tools to build a data-driven story.
This is a coursework course. Coursework carries 100% of the grade and the individual project: written report with innovative visualization designs is the single heaviest piece at 40%, so steady work across the semester decides your result more than any one sitting. Continuous.
Final exam timing: No examination. Confirm the exact date and venue on your exam timetable.
How to actually pass it
A weekly rhythm, two checklists, and the traps to avoid
The course rewards consistency over cramming, and practice over re-reading. Here is the loop that works, then what to have nailed before each exam.
The weekly loop
The mistakes that cost marks
Formula & concept sheet
The vocabulary and formulas you must own
- Data-ink ratio
- The share of a chart's ink that carries data; a design principle for removing clutter.
- Dashboard
- A single screen of linked visuals for monitoring key measures.
- ggplot2
- An R package that builds charts from layered grammar-of-graphics components.
- Infographic
- A designed visual that explains a topic with data, text and illustration.
- Small multiples
- A grid of the same chart repeated across categories for comparison.
- Network graph
- Nodes and edges drawn to show relationships such as a social network.
- Interactive visualization
- A chart the reader can filter, hover or drill into.
- Data-driven story
- A narrative built from a sequence of visuals that lead to a conclusion.
Set texts
The prescribed reading
The syllabus references map straight onto these.
Fischetti & Lantz, R: Data Analysis and Visualization, Packt, 2016
.
Dobler & Grossmann, Data Visualization with Python, Packt, 2019
.
Where it fits
Prerequisites, related courses & why it matters
No prerequisites; 3 credit units; postgraduate; offered Semester A 2026/27. No programming background required.
Your IS6335 study toolkit
Study the course with Sia, not just read about it
Use your permitted course notes with these study tools. Follow your course’s rules for AI and assessed work.
FAQ
Frequently asked questions
Is IS6335 hard?
Moderate on the six-factor rubric and below the HK anchor: no exam and a 50% floor, but two design-judged projects and a step up into R and Python.
What is the assessment breakdown?
Class participation 10%, assignments 30%, group project 20%, individual project 40%; 100% coursework, minimum 50% to pass, per the CityU syllabus.
Do I need to code?
The syllabus says no programming background is required. You will use Tableau first, then R and Python packages introduced in lectures and practised in tutorials.
Which textbooks?
Two compulsory: Fischetti and Lantz on R data analysis and visualization, and Dobler and Grossmann on data visualization with Python. Yau, Few, Wickham, Murray and Evergreen are additional.
What is the group project?
Your group finds a data source and builds an analytics report told as a data-driven story with the visualization tools; it is 20%.
How much class time?
One hour of lecture and two hours of tutorial per week.
Study IS6335 with Sia
Practise explaining the course concepts and check your reasoning. Confirm assessed-work requirements in your current outline.
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