Difference between revisions of "Group02 proposal"
Lxguo.2017 (talk | contribs) |
Lxguo.2017 (talk | contribs) |
||
Line 131: | Line 131: | ||
||Software Challenge||Unfamiliarity of visualisation tools such as R, R Shiny, Tableau. | ||Software Challenge||Unfamiliarity of visualisation tools such as R, R Shiny, Tableau. | ||
|| | || | ||
+ | * Github Learning | ||
+ | * Stackoverflow research | ||
* Self-directed and peer learning | * Self-directed and peer learning | ||
* Watch video tutorials from YouTube | * Watch video tutorials from YouTube | ||
− | |||
− | |||
* Hands-on practice using the different training platforms such as Data Camps | * Hands-on practice using the different training platforms such as Data Camps | ||
|- | |- | ||
Line 141: | Line 141: | ||
||Inexperince with data cleaning and transformation using R | ||Inexperince with data cleaning and transformation using R | ||
|| | || | ||
− | |||
* Trial and error | * Trial and error | ||
* Read online articles and forums for guidance | * Read online articles and forums for guidance | ||
+ | * Watch video tutorials on how to fully utilise packages such as lapply, tidyr and dplyr | ||
|- | |- | ||
| 3. | | 3. | ||
Line 153: | Line 153: | ||
|- | |- | ||
| 4. | | 4. | ||
+ | || Dataset Complexity | ||
+ | || Our have different data from multiple sources in multiple different formats, hence we foresee a huge challenge in standardizing the data | ||
|| | || | ||
− | + | * Make use of data preparation tools such as tableau prep | |
− | + | * Make use of our database management skills to normalize all data tables into third normal form | |
|- | |- | ||
|} | |} |
Revision as of 15:47, 29 February 2020
Contents
PROBLEM & MOTIVATION
When it comes to purchasing or renting a property, there are many factors that go into a buyer’s consideration before he makes the final decision. The primary concern for buyers is the pricing of the property [1].
However, our group identified that there are also secondary concerns such as the weather and the amenities available that do influence the buyer’s final decision to purchase the property.
There are limited tools available to help property buyers to identify areas that suit their needs/preferences best. The current tools that are available are only optimal to suit one category of concern, but fails when we try to use more categories to make our visualization.
For example, different people have different preferences for the weather. Some may prefer sunny weather while others prefer it to rain all the time. Currently, we are able to find weather information, and property prices information, but not both at the same time.
Through the visualization, our group hopes to be able to better allow for users to be able to visualize a home of his dreams.
OBJECTIVES
Target Group:
DATASET
Data/Source | Variables/Description | Methodology |
---|---|---|
|
|
BACKGROUND SURVEY OF RELATED WORK
Reference of Other Interactive Visualization | Learning Points |
---|---|
Title: |
|
Title: |
|
Title: |
|
Title: |
|
REFERENCE LIST
References
KEY TECHNICAL CHALLENGES & MITIGATION
No. | Challenge | Description | Mitigation Plan |
---|---|---|---|
1. | Software Challenge | Unfamiliarity of visualisation tools such as R, R Shiny, Tableau. |
|
2. | Programming Challenge | Inexperince with data cleaning and transformation using R |
|
3. | Workload Constraint | Time and Workload Constrains |
|
4. | Dataset Complexity | Our have different data from multiple sources in multiple different formats, hence we foresee a huge challenge in standardizing the data |
|
STORYBOARD
Dashboards | Description |
---|---|
Dashboard 1: |
|
Dashboard 2 |
|
Dashboard 3: |
|
MILESTONES
COMMENTS
No. | Name | Date | Comments |
---|---|---|---|
1. | (Name) | (Date) | (Comment) |
2. | (Name) | (Date) | (Comment) |
3. | (Name) | (Date) | (Comment) |