Description

COMP30760 Assignment 1 

Overview: The objective of this assignment is to collect a dataset from one or more open web APIs of your choice, and use Python to prepare and analyse the collected data. The assignment should be implemented as a single Jupyter Notebook (not a script). Your notebook should be clearly documented, using comments and Markdown cells to explain the code and results. Tasks: In this assignment you should complete the following tasks: 

1. Data identification: 

• Choose at least one open web API as your data source (i.e. not a static or pre-collected dataset). If you decide to use more than one API, these APIs should be related in some way. 

2. Data collection: 

• Collect data from your API(s) using Python. Depending on the API(s), you may need to repeat the collection process multiple times to download sufficient data. 

• Store the collected data in an appropriate file format for subsequent analysis (e.g. JSON, XML, CSV).

 3. Data preparation and analysis:

 • Load and represent the data using an appropriate data structure (i.e. records/items as rows, described by features as columns).

 • Apply any preprocessing steps that might be required to clean or filter the data before analysis. Where more than one API is used, apply suitable data integration methods. 

• Analyse, characterise, and summarise the cleaned dataset, using tables and plots where appropriate. Clearly explain and interpret any analysis results which are produced. 

• Summarise any insights which you gained from your analysis of the data. Suggest ideas for further analysis which could be performed on the data in future. 

 Guidelines: - The assignment should be completed individually. Any evidence of plagiarism will result in a 0 grade. - Submit your assignment via the COMP30760 Brightspace page. Your submission should be in the form of a single ZIP file containing the notebook and your data. If your data is too large too upload, please include a smaller sample of the data in the ZIP file. - In the notebook please clearly state your full name and your student number. Also provide links to the home pages for the API(s) which you used. - Hard deadline: Submit by end of 4th November 2019 - 1-5 days late: 10% deduction from overall mark - 6-10 days late: 20% deduction from overall mark - Assignments will not be accepted after 10 days without an extenuating circumstances form and/or a medical certificate.