MBA 8020 (Data Visualization) *Data Scientist Track*
Fall 2023 Updated on Aug 14, 2023 - Section 4 (CRN: 88779): Tuesdays 12:00 pm - 2:30 pm
- Section 9 (CRN: 93223): Tuesdays 3:00 pm - 5:30 pm
| Office Hours | Link | ||
|---|---|---|---|
| Saber Soleymani (instructor) | ssoleymani@gsu.edu | Mondays 2:00 - 3:00 pm | Webex Meetings |
| Sai Roopesh Mandava (Teaching Assistant) | smandava4@student.gsu.edu | Thursday 2:00 - 3:00 pm |
Course Description
The Data Visualization course aims to equip students with the skills and knowledge to effectively represent complex data in a visually intuitive manner. The course covers a range of topics from foundational elements of data visualization to advanced techniques and tools. Students will engage in hands-on activities and a final group project to apply these techniques to real-world data sets.
Learning Objectives
By the end of the course, students should be able to:
- Understand the fundamentals of data visualization, including data taxonomy and basic data manipulation in Python.
- Distinguish between different types of data visualizations such as scalar and vector field visualizations.
- Apply interactive visualization techniques to large-scale data sets.
- Evaluate the perceptual issues in data visualization and their impact on the effectiveness of visual representations.
- Utilize specialized tools and frameworks for creating advanced visualizations.
- Create narrative visualizations that effectively communicate data stories.
- Understand the ethical considerations in data visualization.
Textbooks
- Paczkowski, Walter R. Business Analytics: Data Science for Business Problems. Springer Nature, 2022.
- Wes McKinney Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter. O’Reilly Media, 2022. Available here
Note: This course does not require the purchase of a textbook.
Weekly Schedule
| Date | Week Number | Location | Main Topic | Subtopics |
|---|---|---|---|---|
| 10/10 | Week 1 | Online | Course Introduction | Overview, Syllabus Review, Introduction to Matplotlib |
| 10/17 | Week 2 | In-person | Foundations of Data Visualization | Data Taxonomy, Data Manipulation in Python, Scalar Field Visualization |
| 10/24 | Week 3 | In-person | Python for Data Visualization | Data Manipulation Continued, Vector Field Visualization |
| 10/31 | Week 4 | Online | Interactive Visualizations | Introduction to Interactive Visualization, Large-Scale Data Visualization |
| 11/07 | Week 5 | In-person | Advanced Interactive Visualizations | Narrative Visualization, Perceptual Issues in Visualization |
| 11/14 | Week 6 | In-person | Specialized Tools and Frameworks | Tools, Graph Visualization, Geospatial Visualization |
| 11/28 | Week 7 | Online | Final Project Presentations | Presentation of Final Projects |
General Topics Covered
- Data Taxonomy and Basic Data Manipulation in Python
- Scalar Field Visualization
- Vector Field Visualization
- Large-Scale Data Visualization
- Interactive Visualization
- Narrative Visualization
- Perceptual Issues in Visualization
- Specialized Tools for Data Visualization
- Graph Visualization
- Geospatial Visualization
Deadlines
| Assignment | Due Date | Week Aligned With |
|---|---|---|
| Reading 1 | Due 10/23 (before Week 3 starts) | Week 2 |
| Individual Assignment | Due 11/06 (before Week 5 starts) | Week 4 |
| Quiz & Make-up Quiz | From 11/10 to 11/14 (noon) | Week 5 |
| Final Group Project | Due 11/28 (Week 7) | Week 7 |
| Bonus: Reading 2 | Due 11/30 |
The weekly schedule and deadlines are tenative and subject to change. Please check the updates on iCollege.
Grading Policy
Letter Grade Breakdown
| A+ (97-100+) | B+ (87–89.4) | C+ (77–79.4) | D (59–69.4) |
| A (91–96.9) | B (83–86.9) | C (72–76.9) | F (0–59.9) |
| A- (89.5–90.9) | B- (79.5 –82.9) | C- (69.5 –71.9) |
Points Breakdown
| Points | Assignment |
|---|---|
| 15 | Reading Reflection 1 |
| 25 | Invidual Assignment |
| 15 | Quiz |
| 35 | Final Group Project |
| 10 | Participation & Discussions |
| 5 | Bonus: Reading Reflection 2 |
Course Policies
Attendance Policy
Students are expected to attend all in-person and online classes to gain the full benefit of the course. However, missing one class without explanation is accepted for this course. While individual attendance will be tracked, it will not be graded directly. If you are unable to attend a class, you need to study the materials of the class you missed and engage more in discussions in future classes.
Late/Missed Work Policy
Assignments that are turned in late are subject to a late penalty. 20% deduction will be applied to the submission which is submitted late for up to 48 hours after the deadline. After 48 hours, you will automatically get Zero.
Email Policy
As instructors can see more information about students and their enrolled sections, it is preferable to use iCollege’s messaging system rather than emails.
Collaboration Policy
All work is expected to be your own for each exam, quiz, homework, and project. No collaboration is allowed unless otherwise stated in the instructions. If you use a paragraph or code from the internet, you should cite that resource.
Academic Honesty Policy
We must abide by GSU’s Academic Honesty Policy.
Laptop & Technology Statement
You will need a computer to do the assignments. We will be using e-devices in different capacities in the class. Please make sure to silence your e-devices during the meetings.
Online Class Etiquette
Designate Your Workspace
- Treat each online session as if you were present in class.
- Mute your microphone when it is not in use.
- Identifying the speaker is sometimes difficult, especially when the screen is shared. Let others know your name whenever you start speaking.
Syllabus Policy
This course syllabus is a general course plan; the instructor’s deviations may be necessary. The syllabus may be updated throughout the course, so you should check iCollege to ensure you are reading the latest version. I will send an announcement to the class if there has been a major change in the syllabus.