Difference between revisions of "Course information"
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* class participation, | * class participation, | ||
* in-class hands-on exercise, | * in-class hands-on exercise, | ||
| − | * DataViz | + | * DataViz makeover |
* individual assignments, | * individual assignments, | ||
* visual analytics project. | * visual analytics project. | ||
There will be no mid-term test or final examination for this course. | There will be no mid-term test or final examination for this course. | ||
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=== Class Participation === | === Class Participation === | ||
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* Question about the readings or answers to other peoples questions | * Question about the readings or answers to other peoples questions | ||
* Reflection on skills learned through working on an hands-on exercise. | * Reflection on skills learned through working on an hands-on exercise. | ||
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===DataViz Makeover=== | ===DataViz Makeover=== | ||
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The purpose of the makeover is to improve on the original visualisation. Focus on what works, what doesn’t work, why those things don’t work, and how you made it better. You should try stick to the fields in the data set provided and improve upon the original visualisation. However, if supplementing the data helps you tell a better story, go for it! | The purpose of the makeover is to improve on the original visualisation. Focus on what works, what doesn’t work, why those things don’t work, and how you made it better. You should try stick to the fields in the data set provided and improve upon the original visualisation. However, if supplementing the data helps you tell a better story, go for it! | ||
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| + | You are required to upload the weekly makeover onto the dropbox of e_learn (i.e. LMS) by Wednesday before mid-night 11.59. | ||
===Individual Assignments=== | ===Individual Assignments=== | ||
| − | There | + | There is one assignment that are due throughout the term. Students may work together to help one another with computer or Visual Analytics issues and discuss the materials that constitute the assignment. However, each student is required to prepare and submit the assignment (including any computer work) on their own. Cheating is strictly forbidden. Cheating includes but not limited to: plagiarism and submission of work that is not the student’s own. |
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| + | The assignment due are to be uploaded into the Assignment Dropbox strictly before the official due date. Late work, will be severely penalised. Students must check and confirm on Wiki the assignment due date. | ||
| − | + | The assignments will be graded on a scale from 0 to 100. Scores of 70 and 79 are given when the assignment is essentially done completely and correctly. Scores 80 and 100 are reserved for complete and correct homework where extra initiative or innovation clearly sets the completed work above the simple, perfunctory and satisfactory completion of the assignment. | |
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===Visual Analytics Project=== | ===Visual Analytics Project=== | ||
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For more details please refer to [[Visual_Analytics_Project|Visual Analytics Project page]] | For more details please refer to [[Visual_Analytics_Project|Visual Analytics Project page]] | ||
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== Grading Summary == | == Grading Summary == | ||
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The grading distribution of this course is as follows: | The grading distribution of this course is as follows: | ||
| − | * Class Participation | + | * Class Participation 10% |
| − | * | + | * DataViz Makeover 30% |
| − | * | + | * Assignment 20% |
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* Visual Analytics Project 40% | * Visual Analytics Project 40% | ||
| − | ** | + | ** Poster 10% |
| − | + | ** VA research paper 15% | |
| − | + | ** VA application 15% | |
| − | ** VA research paper | ||
| − | ** VA application | ||
There will be no mid-term test or final examination for this course. | There will be no mid-term test or final examination for this course. | ||
Latest revision as of 10:02, 15 May 2017
ISSS608 Visual Analytics and Applications
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Contents
Synopsis
In this competitive global environment, the ability to explore visual representation of business data interactively and to detect meaningful patterns, trends and exceptions from these data are increasingly becoming an important skill for data analysts and business practitioners. Drawing from research and practice on Data Visualisation, Human-Computer Interaction, Data Analytics, Data Mining and Usability Engineering, this course aims to share with you how visual analytics techniques can be used to interact with data from various sources and formats, explore relationship, detect the expected and discover the unexpected without having to deal with complex statistical formulas and programming.
The goals of this course are:
- To share with you the principles, best practices and methods of visual analytics
- To provide you hands-on experiences in using commercial-off-the-shelf visual analytics software and programming tools to design visual analytics applications
Learning Objectives
Upon successful completion of the course, students will be able to:
- Understand the basic concepts, theories and methodologies of visual analytics.
- Analyse data using appropriate visual thinking and visual analytics techniques.
- Present data using appropriate visual communication and graphical methods.
- Design and implement cutting-edge visual analytics system for supporting decision making.
Basic Modules
This course comprises ten integrated components as shown below:
Course Assessment
The assessment of this course consists of four major components, namely:
- class participation,
- in-class hands-on exercise,
- DataViz makeover
- individual assignments,
- visual analytics project.
There will be no mid-term test or final examination for this course.
Class Participation
A strict requirement for each class meeting is to complete the assigned readings and to try out the hands-on exercises before coming to class. Readings will be provided from the textbook on technical information and from provided documents and articles on business applications of Visual Analytics. Students are required to review the recommended readings and class exercises before coming to class. Without preparation, the learning and discussions would not be as meaningful. Student sharing of insights from readings and hands-on exercises of assigned materials in class participation will form a large part of the learning in this course.
In this course class participation includes participation in the discussion on course wiki. All students are required to post at least one substantive discussion comment or question pertaining to each lesson, set of readings, and hands-on exercise. Comments or questions for each lesson must be posted within one week after the lesson.
Examples of good comments include and not confine to the followings:
- Clarification of some points or details presented in the class
- Links to web resources or examples that pertain to a lesson or reading with reasons
- Question about the readings or answers to other peoples questions
- Reflection on skills learned through working on an hands-on exercise.
DataViz Makeover
On Sunday of each week, I will post one or two data visualisation and you are required to critic, suggest ways for improvement and rework the data visualisation. Maybe you retell the story more effectively, or find a new story in the data. I am curious to see the different approaches you all take.
The purpose of the makeover is to improve on the original visualisation. Focus on what works, what doesn’t work, why those things don’t work, and how you made it better. You should try stick to the fields in the data set provided and improve upon the original visualisation. However, if supplementing the data helps you tell a better story, go for it!
You are required to upload the weekly makeover onto the dropbox of e_learn (i.e. LMS) by Wednesday before mid-night 11.59.
Individual Assignments
There is one assignment that are due throughout the term. Students may work together to help one another with computer or Visual Analytics issues and discuss the materials that constitute the assignment. However, each student is required to prepare and submit the assignment (including any computer work) on their own. Cheating is strictly forbidden. Cheating includes but not limited to: plagiarism and submission of work that is not the student’s own.
The assignment due are to be uploaded into the Assignment Dropbox strictly before the official due date. Late work, will be severely penalised. Students must check and confirm on Wiki the assignment due date.
The assignments will be graded on a scale from 0 to 100. Scores of 70 and 79 are given when the assignment is essentially done completely and correctly. Scores 80 and 100 are reserved for complete and correct homework where extra initiative or innovation clearly sets the completed work above the simple, perfunctory and satisfactory completion of the assignment.
Visual Analytics Project
The purpose of the project is to provide students first hand experience on collecting, processing and analysing large business data using real world data. A project may involve developing new methods or implementing visual analytics system to support analytic tasks in specific domains. Alternatively, a project may be in the form of application development by integrating analytical tools within a visual analytics environment. Students are encouraged to focus on research topics that are relevant to their field of study. It should address a concrete visual analytics problem and should propose a novel and creative solution.
For more details please refer to Visual Analytics Project page
Grading Summary
The grading distribution of this course is as follows:
- Class Participation 10%
- DataViz Makeover 30%
- Assignment 20%
- Visual Analytics Project 40%
- Poster 10%
- VA research paper 15%
- VA application 15%
There will be no mid-term test or final examination for this course.
