Programming for Data Science (PDS)
With the use of datasets from data.gov.sg I have created 6 graphs with insightful analysis.
This was done by using jupyter notebook and python with libraries such as pandas, matplotlib,
numpy and seaborn. Through this project, I have gain the fundamental skills to code applications
to retrieve, clean and visualize data using the Python programming language. As well as, the
ability to apply Python packages to describe data, explore data and to create visualizations to
gain useful insights from it.
What is the relationship between the Government Health Expenditure and the number of Medical Professionals?
With the dataset of “Doctors per 10000 Total Population” I have created a line graph to show the
Medical Professionals in Singapore between 1990 to 2019. Through this graph I can tell that there
has been a general steady increase of Medical Professionals from 1990 with 48 per 10000 total
population to 2019 with 104 per total population. However, between 2008 and 2009, there was a sharp
increase of 15 medical professionals per 10000 total population.
With the dataset of “Government Health Expenditure” I have created a bar graph to display the operating
expenditure and development expenditure in healthcare. With the bar graph, I can tell that there has
been a constant increase in Operating and Development Expenditure on Health from 2006 to 2017. With a
significantly larger amount on the Operating Expenditure compared to the Development Expenditure.
However, there was a decrease in Development Expenditure between 2009 and 2010 with a drop of S$226 million.
With the use of the 2 datasets above, I created a data frame for “Medical Professionals and Government
Health Expenditure”. I plotted the data onto a scatterplot to allow a simple data analysis of the
relationship between the Government Health Expenditure and Number of Medical Professionals. From this
scatter graph, I can conclude that there is a positive relationship between Government Health Expenditure
and the Number of Medical Professionals. This means that as the government spent more on Health Expenditure,
the number of Medical Professionals in Singapore will increase.
What is the relationship between the number of dengue outbreak and total amount of rainfall?
With the dataset of “Rainfall Monthly Total“ I have created a bar graph with a colour map which allow the
user to easily find out which has the most and least amount based on the darkness of the colour of the bar.
From the bar graph, I can tell that the trend of rainfall is that it is consistent in the middle of the year
and it increases during the end and at the start of the year. The highest months are December, November, and
January. The lowest month is February.
With the dataset of “Dengue Outbreak Statistics” I have created a line chart with a vertical line to
highlight the year with the most outbreak. Through this chart, I can tell that trend of dengue outbreak
between 2007 to 2015 is not consistent. From 2007 to 2009 there was a gradual decrease in dengue outbreaks
from 180.6 to 83.9 dengue cases per 100,000 population. From to 2009 to 2012 the dengue outbreak stayed
stable. Between 2012 to 2013, there a huge increase in number of dengue outbreak from 82.7 to 404.9 dengue
cases per 100,000 population. After 2013, the dengue outbreak has been gradually decreasing.
With the use of the 2 datasets above, I created a data frame for “Dengue Outbreak And Total amount of
Rainfall”. With the data, I plotted a scatterplot to perform a simple data analysis of the relationship
between the amount of rainfall yearly and the number of dengue outbreaks. From this scatter plot, I can
tell that there is slim or no correlation between the amount of total amount of rainfall each year and
the number of dengue outbreaks. This is because the best fit line is almost a horizontal line. Due to many
points being in a straight line with multiple anomalies.
