Reference no: EM132334650

Introduction to Data Science Assignment - Description

The aim of this assignment is to investigate and visualise data using various data science tools. It will test your ability to:

• read data files in Python and extract related data from those files;
• wrangle and process data into the required formats;
• use various graphical and non-graphical tools to performing exploratory data analysis and visualisation;
• communicate your findings in your report.

You will need to submit two files:

1. A report in PDF containing your answers to all the questions. Note that you can use Word or other word processing software to format your submission. Just save the final copy to a PDF before submitting. Make sure to include screenshots/images of the graphs you generate in order to justify your answers to all the questions.

2. The Python code as a Jupyter notebook file that you have written to analyse and plot the ata.

Tasks -

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 from Moodle:

• 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 fordifferences in purchasing power (in international dollars) across different countries around the world, for the period between 1800 and 2018.

A1. Investigating the 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 United States over time.

• What are the maximum and minimum values for gender ratio in Australia over the time period?
• 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:

• Which two countries generally have the lowest gender ratio (women % men) in schools?
• 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.
• 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.
• 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 on 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. Unzipped, the file is over 500MB and contains the following fields:

Load the InsuranceRates.csv data in Python and answer the following questions:

B1. How many years does the 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? 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.) 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 on 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,

Please note that your analysis should at least contain visualisation, interpretation of your visualisation and a prediction task.

Attachment:- Data Science Assignment File.rar