Project 1560

Project Info

Team Name

Optimo GovHack

Team Members

Jethro Revalde , Nathan Williams , Cindy , Allen , Dean Nguyen

Project Description

Over the GovHack weekend, we first viewed the challenges as soon as they were released on Friday night. After selecting two challenges we worked on brainstorming for the rest of the night while also looking over the available datasets. Once we happy with our night’s work we called it a night and prepared for the next day.
On Saturday we finalized our ideas and started prototyping to demonstrate what we hoped to achieve over the weekend. Saturday involved further reading of datasets as well as sanitizing and normalizing the data so they it could be represented more accurately. I also had to learn how to import our data from a .csv format into Neo4J so that it could be imported easily enough.
Sunday was all about finalizing our ideas and bringing them to life as well as polish. It was about solidifying our ideas so that they can be used to showcase our points.

On first day, me and my teammate come through several topics, from post life during pandemic, to youth education and providing assistance to jobs. Finally, after a long-time discussion and debate, me and Jethro focus on the topic of how to improve the Australian Skills Classification.
First, we draw up our layout on mockflow.com, which is a free website that we can draw anything from scratch and share it to our teammates. We share our ideas and make it visualization.
After that, Cindy joined us to code it to a real website. Since it is not a simple page, we separate our task into different pages. Cindy started the repo, and we did our own part.
We code to the late night on Saturday. And merge our code on Sunday.
We have met some issues on GitHub push, after consulting our teammate, we finally managed to push it and merge everything together. After that, we deploy it on Netlify, which is also a free host website. And we take video of our progress and mockup pages to make everything visualization.

Over the weekend we viewed the challenges as soon as they were released on friday night. we discucssed them on slack and zoom. We looked at the available data given by each challenge.

Data Story

*Further Developments *
Neo4j has allowed us to collect and store data that can then be used to visually represent the amount of fluctuation in total school students at particular schools over a period of time. Utilizing Neo4j means we do not have to deal with joins in our select statements, instead we are able to visually see the relationships between nodes in an easier to understand format.

Group data points/nodes into LGA, this would allow for a better visualisation of the total number of students attending schools within a certain LGA.
Add further data points to the database to show amounts of people that are migrating to and from LGA.

Within the limited time, we can only share our basic idea and structure of this mockup. Given more time we can improve our UI, and change it to a more user-friendly one, responsive and accessible to everyone.
And providing the authentication of the skills classification database, we can make this mockup come into reality.

Future version scope:
Connect our site to TAFE or other skills training providers: so that users can click the skills they need from the result page, directly to the webpage where they can offer training.
Connect to social media: Users can share they skills classification result to any social media, asking for advice or looking for someone who also want a career transition
Comment message function on each skill category: So users from different field can share their thoughts about their current occupation, which might be useful to someone want to take a career change
Mobile version App: we could have our mobile app so people can contact us more conveniently.

Refer to our share Google Docs link for further evidence of work:
https://drive.google.com/drive/folders/12jGPHT9VFBpAr-jbQneaJBmVY7rDM7Gw?usp=sharing

Team DataSets

NSW government school enrolments by head count (2004-2018)

Description of Use The data provided by this dataset was used to pair up the headcount for dataset 11. The data correlated with the 2021 master dataset (Data set 11), dataset 11 provided a large sample size for total enrolled students having 16 of the last 18 years worth of data available. Using Neo4j we were able to add a relationship/connection between schools and their yearly attendance.

Data Set

Master dataset: NSW government school locations and student enrolment numbers

Description of Use This data set was used to establish the headcount for each school during the current school year. This data was also used to model the School nodes as the dataset contained the name, suburb and postcode of each school. This data was used alongside dataset 12 to associate the yearly headcount of schools over a period of 2004 - 2018 and 2021. These figures can be used to map either the increase or decrease in students, it can help estimate population growth and future funding. This data was imported into Neo4j so that we could easily visualise the data and identify trends that may emerge over the recorded periods.

Data Set

Australian Skills Classification

Description of Use **Problem/issue 1: ** We extract the information from this dataset without change to work on the website app which we attempted to provide the public with more effective functionality and features. **Problem/issue 2: ** The provided dataset appears to be normalised to some extent. It seems there are still some redundancies of data. **Hack: ** We normalised it further but not necessary to 3NF. The reason behind this is because we do not plan to store the data in a relational database (such as MySQL). Instead, we are going to import the data into Neo4j graph database to present the information visually. The original excel file has eight sheets. The first one provides descriptions in a Table of Contents format. We are going to normalise the remaining sheets into a normalised database. *Original worksheets:* Occupation_Descriptions (ANZSCO_Code, ANZSCO_Title, ANZSCO_Desc) Core_Competencies (…) Core_Competencies_Descriptions (..) Specialist_Tasks (…) Technology_Tools (…) Tech_Tools_Example_Tools (…) Common_Tech_Tools (…) *Our new tables: * Occupations (ANZSCO_Code [PK], ANZSCO_Title, ANZSCO_Desc) There is no change to this actually. This is now ready to be exported into CSV format. Competencies (Competency_ID [PK], Competency_Name, Competency_Description) Scores (Score_ID [PK], Proficiency_Level) Occupations_Competencies_Scores (ANZSCO_Code [FK], Competency_ID [FK], Score_ID [FK], Anchor_Value) We then imported these csv files into Neo4j Desktop and use Cypher to display the information visually. Watch our video for some demo. In summary, instead of importing the dataset into a Relational Database then requiring developers to write SQL queries to extract the data THEN use Tableau or the like to visually present the information.... Our hack shows an easier and more intuitive way to enable users to work the data visually :)

Data Set