Various Visualizations on Tableau. Links to my public Tableau Profile, as well as to any data processing/cleaning done on programs other than Tableau should be found here.
- Tableau Visualization Link
- Jupyter Notebook link, where some Data cleaning and processing was done.
Data was acquired from the datasets found here: Trunk Bus Data, and Express Bus Data
The data was then processed and cleaned through Python, then visualized on Tableau.
In particular, we looked at how Bus Fares varied for 5 different kinds of passengers:
- Regular Adult Fares
- Student Fares
- Senior Citizen Fares
- People with Disabilities
- Holders of Workfare Transport Concession Card, broadly refered to as 'Workers' in the project.
The two Bus Types we looked at were Express Buses and Trunk Buses.
Trunk buses navigate through an extensive network of routes, often passing through several different towns and estates. Express Bus Services are bus services that contain express sectors, by skipping bus stops or travelling by expressways, saving commuting time as compared to regular trunk services. These bus services charge Express fares.
Fares for Feeder Bus services can be found here. Since the fares for these are completely uniform and independent of distance travelled, they were not included in the visualization.
Click the Tableau Visualization Link to see the Dashboard.
Data from data.gov.sg was found here.
Characteristics of HIV occurences over that period were broken down by year and scrutinized through a Dashboard.
Generally, some trends seem to consistently hold true:
- HIV victims are overwhelmingly male (generally 90-95%)
- Ethnic compositions of HIV victims largely mirror Singapore's actual ethnic composition. In other words, there isn't a clear link between ethnic groups and HIV occurrence.
- Heterosexual Sex and Homosexual Sex are the primary modes of transmission, generally both accounting for 85-90% of all HIV occurrences together.
Some particularly noteworthy points were the following:
- 2009 saw the highest proportion of female victims of HIV among all the 10 years, at 9.7%. During this year, Heterosexual Sex was also the leading mode of transmission by a very significant margin, accounting for 61.3% of occurrences.
- In 2013, 2015, 2016, and 2017, homosexual sexual encounters were the leading modes of transmission, exceeding heterosexual sex.
- In the last 3 years on the dashboard especially, namely 2015, 2016, and 2017, Homosexual sex became the leading mode of transmission by a significant margin. This could be indicative of a rise in the tendency towards casual and unsafe sex among the demographic of homosexual males.
The visualization can be found here.
The dataset used can be downloaded here
Data from a bank was used to analyze the demographics of its customers using a Tableau Dashboard to find out more about how they varied in each of the 4 regions.
- Customers in Scotland are generally Male, above 40 years old, and predominantly Blue Collar workers.
- Customers form England have a high proportion of white collar workers, and the age distribution leans more towards people in their 20's or 30's.
- Since Scotland and England have both the biggest customer-bases by a margin, but they are very different demographiaclly, it would make sense to adjust marketing and advertising tactics to suit each of those customer-bases differently.
- For instance, customers in Scotland could be encouraged to save to plan ahead for retirement, and to take care of their families.
- Customers in England could be motivated to save money for further education, and given further incentives like possibilities for loans to buy property with (since a lot of them are young people liekly just entering the workforce).
Click the Tableau Visualization Link to see the Dashboard.
- Tableau Dashboard Link
- A dataset containing information on billionares was found here, and then imported into Python for some quick processing, before being used in Tableau for creation of the dashboard.
- Tableau Dashboard Link
- A table from the nea.gov.sg site was scraped for information on Dengue Occurrences by location. A Python Script (linked in this repository) was written to extract the information from that table and convert it to a csv format that could be worked with smoothly in Tableau. This Python script was automated to run weekly, on every Monday, to collect the newest data available.