IT5425 Data management and visualization
Quản trị dữ liệu và trực quan hoá
- Credits: 2(2-1-0-4)
- Prerequisite: None
- Pre-courses: None
- Corequisite: None
Objectives: This course provides basic foundations on data management and visualization. Students are trained to design and propose solutions to store, manage, and integrate data, and to visually present a story from the data and insights. Real-life applications and datasets are used for practice with Python and its libraries. By the end of the course, students can choose appropriate solutions and tools for storing and visualizing data in real-world problems.
Mục tiêu: Môn học cung cấp nền tảng về quản trị và trực quan hoá dữ liệu, từ tổ chức và làm sạch dữ liệu đến phân tích thăm dò và kể chuyện bằng dữ liệu. Sinh viên thực hành trên Python và tập dữ liệu thực tế.
Course content
Nội dung giảng dạy
Chapter 1: Overview of data visualization
Chương 1: Tổng quan về trực quan hoá dữ liệu
- 1.1. Introduction
- 1.2. Why data visualization
- 1.3. Role of data visualization
- 1.4. Types of data visualization
Chapter 2: Visual models and encoding
Chương 2: Mô hình và mã hoá trực quan
- 2.1. Properties of data and measurement levels
- 2.2. Visual marks
- 2.3. Visual channels
- 2.4. Visual encoding
Chapter 3: Graphical perception
Chương 3: Nhận thức trực quan bằng đồ hoạ
- 3.1. Signal detection
- 3.2. Magnitude estimation
- 3.3. Pre-attentive processing
- 3.4. Using multiple visual encodings
- 3.5. Gestalt grouping principles
Chapter 4: Visualization for multi-dimensional data
Chương 4: Trực quan hoá dữ liệu đa chiều
- 4.1. Coordinate systems and axes
- 4.2. Visualizing amounts
- 4.3. Visualizing distributions
- 4.4. Visualizing proportions
- 4.5. Visualizing relationships
- 4.6. Visualizing trends
- 4.7. Visualizing uncertainty
Chapter 5: Visualization for graphs
Chương 5: Trực quan hoá dữ liệu đồ thị
- 5.1. Graph data properties
- 5.2. Mechanisms for graph visualization
Chương 6: Nguyên tắc thiết kế hình vẽ
- 6.1. The principle of proportional ink
- 6.2. Handling overlapping points
- 6.3. Issues in the use of color
- 6.4. Multi-panel figures
- 6.5. Titles, captions, and tables
- 6.6. Balancing data and context
- 6.7. Problems with 3D charts
Chapter 7: Map visualization
Chương 7: Trực quan hoá bản đồ
- 7.1. Theoretical foundations
- 7.2. Types of map data visualization
Chapter 8: Interactive visualization
Chương 8: Trực quan hoá tương tác
- 8.1. Introduction
- 8.2. Design principles
- 8.3. Interaction techniques: filtering, zooming, selection, view transformation
- 8.4. Animated visualization
- 8.5. Tools and libraries
- 8.6. Practical applications
Chapter 9: Storytelling with data
Chương 9: Kể chuyện bằng dữ liệu
- 9.1. Introduction
- 9.2. Fundamental principles of data storytelling
- 9.3. Visualization techniques for storytelling
- 9.4. Narrative styles in data storytelling
- 9.5. Interaction and exploration in storytelling
Capstone project guidelines
Hướng dẫn bài tập lớn
Project requirements
Yêu cầu dự án
Build a dashboard for a specific dataset.
- Tools: Power BI, Apache Superset, Tableau, Python (Plotly, Dash, Streamlit), or equivalent.
- Data source: The dataset must be findable on Google Dataset Search.
Deliverables
| Item |
Description |
| Source code |
Dashboard source code (or project file if using Power BI, Tableau) |
| Presentation slides |
Slides for dashboard demo |
| Report |
Report that clearly analyzes the visualization techniques and guidelines from the course and how they were applied in the dashboard |
Guideline: Writing the “Technique application” section in the report
Hướng dẫn viết phần “Áp dụng kỹ thuật” trong báo cáo
The report must include a clear analysis of the visualization techniques and principles covered in the course, and how they were applied to each chart or page in the dashboard. Use the structure below — include a subsection for each relevant chapter.
Suggested structure per chapter
### Chapter X: [Chapter title]
#### Techniques / principles applied
- List the relevant items (X.1, X.2, ...) from the chapter that relate to your dashboard.
#### How applied in the dashboard
- Describe specifically: which chart/page, which technique, why that choice.
- You may include screenshots.
#### Notes / adjustments (if any)
- If you deviated from the theory, explain why.
What to cover by chapter
| Chapter |
What to address in the report |
| Ch. 1 |
Why you chose the dataset; role of visualization in the problem; types of visualization used (exploratory, explanatory, …). |
| Ch. 2 |
Data types (nominal, ordinal, quantitative); visual marks (points, lines, areas) and visual channels (position, size, color, …) used; rationale for encoding each variable. |
| Ch. 3 |
Use of pre-attentive processing; Gestalt principles (proximity, similarity, …) in layout; magnitude estimation (length vs area vs angle) — which charts, and whether appropriate. |
| Ch. 4 |
Chart types for amounts, distributions, proportions, relationships, trends; coordinate systems; whether uncertainty was visualized. |
| Ch. 5 |
(If using graph data) Graph data properties; layout mechanism (force-directed, hierarchical, …); node/edge representation. |
| Ch. 6 |
Proportional ink; handling overlap; use of color (palette, accessibility); multi-panel; titles and captions; data–context balance; avoiding unnecessary 3D charts. |
| Ch. 7 |
(If using maps) Type of map visualization; foundations (projection, geocoding); how data is encoded on the map. |
| Ch. 8 |
Interaction techniques: filter, zoom, select, change view; use of animation; tools/libraries used. |
| Ch. 9 |
Story structure; storytelling principles; narrative style; interaction and exploration in the story. |
Note: You do not need to cover every chapter. Only include chapters relevant to your dashboard; you may skip others or briefly state why they do not apply.
Materials / Tài liệu: Google Drive resources
