<file_sep>import sys

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instructor: <NAME>
email: <EMAIL>
location: Online, Room WEB
time: Thursday 06:00PM - 09:25PM
office hour: By Appointment
---
# DS-620 - Data Visualization

## Course Description

Visualization concerns the graphical depiction of data and information in order to communicate its contents and reveal patterns inherent in the data. It is sometimes referred to as visual data mining, or visual analytics. 

Data visualization has become a rapidly evolving science. This course explores the underlying theory and practical concepts in creating visual representations of large amounts of data. Topics covered include 

- data representation
- information visualization
- real-time visualization
- visualization toolkits including Tableau and their applications to diverse data rich contexts. 

At the end of the course, the student will be able to present meaningful information in the most compelling and consumable fashion.

## Prerequisites

This course requires following courses to be taken before this course.

- Introduction to Data Science (DS-510)
- Data Analysis and Decision Modeling (DS-520) 

## Learning Outcomes

This course aims to teach you the following concepts.

- Understanding basics of data visualization, including data visualization abstractions, data types, 
- Ability to make decision on which data charts are best on different data types
- Data visualization with python using well-known packages, matplotlib, seaborn, etc
- Comprehensive overview of data visualization softwares
- Ability to use Tableau to create dynamic, user-friendly dashboards
  
## Textbooks & Tools

[Visualization Analysis and Design](https://www.routledge.com/Visualization-Analysis-and-Design/Munzner/p/book/9781466508910) by <NAME>.

``` yml
Title: Visualization Analysis and Design
Author(s): <NAME>
Release date: December 1, 2014
Publisher(s): A K Peters/CRC Press
ISBN: 9781466508910
```

The website by Tamara Munzner also has great content about the book, check it out from [https://www.cs.ubc.ca/~tmm/vadbook/](https://www.cs.ubc.ca/~tmm/vadbook/).

[Handbook of Data Visualization](https://www.springer.com/gp/book/9783540330363) by <NAME>, <NAME>, <NAME>.

``` yml
Title: Handbook of Data Visualization
Author(s): 
    - <NAME>
    - <NAME>
    - <NAME>
Release date: 2008
Publisher(s): Springer-Verlag Berlin Heidelberg
ISBN: 978-3-540-33037-0
```

## Course Outline

Below are the distribution of content and assignments for the semester.

| Week    | Tasks               | Description                                                                                                                            |
| ------- | ------------------- | -------------------------------------------------------------------------------------------------------------------------------------- |
| Week 01 | Assignment0         | VAD Chapter 1. What's Vis, and Why Do It?                                                                                              |
| Week 02 | Assignment1         | Theoretical Intro (Types of Charts, etc.) and Viz with Python 1 (matplotlib, pandas, seaborn)                                          |
| Week 03 | Assignment2         | VAD Chapter 2. What: Data Abstraction, Chapter 3. Why: Task Abstraction and Viz with Python 2 (plotly, bokeh, networkx)                |
| Week 04 |                     | VAD Chapter 4. Validation, Chapter 5. Marks and Channels, Chapter 6. Rules of Thumb, Visualization Tools (Tableau, Qlik, Lookup, etc.) |
| Week 05 | Assignment3         | VAD Chapter 7. Arrange Tables, Chapter 10. Map Color and Other Channels, Tableau 1                                                     |
| Week 06 | Final Project Start | VAD Chapter 8. Arrange Spatial Data, Chapter 9. Arrange Networks, Tableau 2                                                            |
| Week 07 | Midterm             | Midterm (VAD Chapters), FP Session 1                                                                                                   |
| Week 08 |                     | Solving all assignments in class, FP Session 2                                                                                         |
| Week 09 |                     | Tableau 3, FP Session 3                                                                                                                |
| Week 10 |                     | Tableau 4, FP Session 4                                                                                                                |
| Week 11 | Final Project End   | Final Project Presentations, What's Next                                                                                               |

## Grade Determination

The items that student is responsible for this class is the following. Students will be graded according to this chart.

| Item          | Percentage |
| ------------- | ---------- |
| Assignments   | 30 %       |
| Projects      | 30 %       |
| Exam          | 30 %       |
| Participation | 10 %       |
| Bonus (Maybe) | 10 %       |

Partication means attending to discussions in Blackboard, class sessions, etc.

<sub> Above list is subject to change. </sub>

## Grade Scale

Based on the [Grade Determination](#grade-determination), a final letter grade will be assigned to student based on the following scale.

| Min | Max | Grade |
| --- | --- | ----- |
| 94  | 100 | A     |
| 87  | 92  | A-    |
| 83  | 86  | B+    |
| 80  | 82  | B     |
| 77  | 79  | B-    |
| 73  | 76  | C+    |
| 60  | 72  | C     |
| 0   | 59  | F     |

## Attendence Policy

Due to COVID-19, there is no restriction on attending the lecture, however, attending the classes will benefit on your learning curve a lot more instead of watching lectures online. As said, here are the guides:

- We record the classes and you can always revisit or watch previous classes. Links will be shared.
- When attending to class, please keep your **camera on** and your **microphone off**.

## Academic Honesty and Student Conduct

Students need to submit **only their own original work** (e.g. Code, PPT, figures, visualizations…). Student need to familiarize themselves with the academic rules of the University. In case a student is found guilty under the act of plagiarism, his/her test or assignment will be graded zero. 

If plagiarism occurs twice, student will receive ‘F’ grade with immediate effect.  It is expected that everybody turns off/mute all devices that emit sounds and noises that may interrupt the class (e.g. mobile phones, pagers, watch alarms). If an occasion arises, in which a student may need to leave the class to receive a phone call (important call), use rest room or get drinking water, he or she should silently walk out without disturbing rest of the class. 

Working on assignments and project work, that belongs to another course is **STRICTLY NOT ALLOWED**.

Copying your colleagues code is **STRICTLY NOT ALLOWED**. All submissions will be submitted to autodetection plagiarism tool that our university provides!

## Agreement between You and the Instructor

I certify that in this course, my contribution and assignments will be my own work, based on my personal study and/or research and that I am acknowledging all material and sources will be used in its preparation, whether they be books, articles, reports, lecture notes, and any other kind of document, electronic or personal communication. 

I also certify that that in this course, the assignments I will be submitting will not previously been submitted for assessment in any other unit, except where specific permission has been granted from all unit coordinators involved, or at any other time in this unit, and that I have not copied in part or whole or otherwise plagiarized the work of other students and/or persons.


| Student        |                                           | Instructor        |                                           |
| -------------- | ----------------------------------------- | ----------------- | ----------------------------------------- |
| Student's Name | ......................................... | Instructor's Name | ......................................... |
| Signature      | ......................................... | Signature         | ......................................... |

## References

Useful links regarding university.

- Check out more information about the prerequisites on [curriculum of the SPU's data science](https://www.saintpeters.edu/academics/graduate-programs/master-of-science-in-data-science/curriculum/).
- Additional information about this class can be found on [SPU's website](https://spiritonline.saintpeters.edu/), which requires student's login. 
- Blackboard Classroom [DS-620-DLSO-21SPTR: Data Visualization](https://saintpeters.blackboard.com/).
