ICT 583 - Data Science Applications Supplementary Assignment - Murdoch University

Assignment Task

Assignment overview

The healthcare industry has been one of the most prominent beneficiaries of the emergence of data science. Successful applications such as AI-assisted diagnosis and prognosis, Computerized drug discovery, and virtual assistant, etc can greatly improve the patient care and save public money. Your final assignment is to apply your data science knowledge on two healthcare datasets, one is the mammographic masses dataset, the other one is the global burden of disease dataset. The goal of this project is to follow the data science analysis pipeline to answer interesting questions of your own choosing, acquire the data, perform data manipulations, design your visualizations, build your predictive modelling using machine learning techniques and present the results in a report format.

Classification - Mammographic Mass Dataset

Step 1: Get your dataset: You will use one health care dataset called Mammographic Mass Data Set

Step 2: You will raise two interesting questions on the dataset and prepare to answer them in your following analysis via data manipulation, visualization or predictive modeling, etc.

Step 3: Data manipulation and cleaning: Observe your dataset and pre-process the data if necessary and justify.

Step 4: Exploratory data analysis: perform initial investigations on data using summary statistic and visualizations.

Step 5: You will select two classification methods and apply them to the dataset for predictive modeling. The performances of different models should be evaluated.

Step 6: Analyze the results

Step 7: Document all your findings

Clustering - GBD Dataset

Step 1: Get your dataset: You will use one health care dataset about Global Burden of Disease Study (GBD) Data Set from LMS. 

Step 2: You will raise two interesting questions on the dataset and prepare to answer them in your following analysis via data manipulation, visualization or clustering modeling, etc.

Step 2: Data manipulation and cleaning: Observe your dataset and pre-process the data if necessary and justify.

Step 3: Exploratory data analysis: perform initial investigations on data using summary statistic and visualizations.

Step 4: You will select two clustering methods to identify the groups of countries from the dataset. The performances of different models should be evaluated.

Step 5: Analyze the results

Step 6: Document all your findings

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