Highlights
Task A: Investigating Population and Gender Equality in Education
In the task, you are required to visualise the relationship between the population in different countries, the income in different countries and the gender ratio (women % men, 25 to 34 years) in schools of different countries, and gain insights from how these relations and trends change over time. The data files used in this task were originally downloaded from Gapminder. We have extracted the data from the original files and put into a simpler format. Please download the data fromMoodle:
• Population.csv: This file contains yearly data regarding the estimated resident population, grouping by countries around the world, between 1800 and 2018.
• GenderEquality.csv: This data file contains yearly data about the ratio of female to male number of years in school, among 25- to 34-years-olds, including primary, secondary and tertiary education across different countries around the world, for the period between 1970 and 2015.
• Income.csv: This data file contains yearly data of income per person adjusted for differences in purchasing power (in international dollars) across different countries around the world, for the period between 1800 and 2018.
A1. Investigating Gender Equality Data
Have a look at the gender equality data.
1. Use Python to plot the gender ratio (women % men) in schools for Australia, China and the United States over time.
o What are the maximum and minimum values for gender ratio in Australia over the time period?
o How do you compare the trend in gender ratio (women % men) in schools for these three countries over the time period? Which two countries have similar growth trend?
A2. Visualising the Relationship over Time
Have a look at the relationship between gender ratio in schools and income over time
1. Use Python to build a Motion Chart comparing the gender ratio in schools, the income, and the population of each country over time. The motion chart should show the gender ratio in schools on the x-axis, the income on the y-axis, and the bubble size should depend on the population.
2. Run the visualisation from start to finish. (Hint: In Python, to speed up the animation, set timer bar next to the play/pause button to the minimum value.) And then answer the following questions:
o Which two countries generally have the lowest gender ratio (women % men) in schools?
o Select Cape Verde and Bolivia for this question: From which year onwards does Cape Verde start to have a higher gender ratio and a higher income from Bolivia. Please support your answer with a relevant python code and motion chart.
o Is there generally a relationship between the amount of income and gender ratio (women % men) in schools in all countries during the whole period of time? What kind of relationship? Explain your answer.
o Any other interesting things you notice in the data? Please support your answer with relevant python code and/or motion chart.
Task B: Exploratory Analysis of Big Data
In this part, you are required to do some exploratory analysis on the health insurance marketplace data. The file InsuranceRates.csv.zip contains data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace. This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). The data was then published on Kaggle. The file we provide is an extract from the data on Kaggle.
Load the InsuranceRates.csv data in Python and answer the following questions:
B1. How many years doesthe data cover? (Hint: pandas provides functionality to see 'unique' values.)
1. What are the possible values for 'Age'?
2. What are the average, maximum and minimum values for the monthly insurance premium cost for an individual? Do those values seem reasonable to you?
B2. Variation in Costs over Time and with Age
Generate boxplots (or other plots) of insurance costs versus year and age to answer the following questions:
1. Are insurance policies becoming cheaper or more expensive over time?
o Is the median insurance cost increasing or decreasing?
2. How does insurance costs vary with the age of the person being insured? (Hint: filter out the value 'Family Option' before plotting the data.)
o In terms of median cost, do older people pay more or less for insurance than younger people? How much more/less to they pay?
Task C: Exploratory Analysis of Other Data
(Note: This additional task is for those students wishing to get higher grades for their assessment. It is not required to pass the assignment, but it is required to get higher credit.)
Find some publicly available data and repeat some of the analysis performed in Tasks A and B above. Good sources of data are government websites, such as: data.gov.au, data.gov, data.gov.in, data.gov.uk, ...
Please note that your analysis should at least contain visualisation, interpretation of your visualisation and a prediction task.
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