Highlights
Task 1: Clean, organize, validate, and create a schema. (10 points)
The first task of this assignment requires you to prepare a dataset for analysis. Given an example dataset, your task is to re-organize the dataset such that simple analysis can be performed with a spreadsheet. You will also be required to clean the data to ensure consistency and quality of the data itself.
This task is designed to assess the following learning outcomes:
Each student will be supplied with a different source spreadsheet and you are required to submit back a spreadsheet that has been prepared appropriately for data analysis. This spreadsheet is located at the end of Module 1. You will need to pay careful attention to the layout of the spreadsheet, the data types used, and the variation of the data.
You may have noticed that this task only carries a third of this assignment’s mark, whereas in the course materials it is suggested that cleaning and preprocessing data takes 80% of the effort. This is due to the fact that this is a fairly mechanical process.
Task 2 (10 points)
In 2013, it was announced that cuts to the London Fire Brigade meant that ten fire stations in the capital had to be closed.
These closures happened a year later in 2014, but what actually happened as a result of the closures? What more should be done to improve the efficiency and overall performance of the London Fire Brigade?
In the next task, you'll explore the answers to these questions, starting this week with analyzing what actually happened.
This task requires you to apply a number of data analysis techniques on the London Fire Brigade performance data to analyze the impact of the fire station closures.
You must quantify the impact by analyzing the most up to date data.
The task is designed to assess the following learning outcomes:
To examine your ability to gather data - an essential part of data science - we have not provided links to the datasets. However, we will point you to a tool built by the Open Data Institute prior to the station closures that predicted the impact of closing the proposed fire stations at the borough level.
You can explore the ODI tool here.
A key skill of a data scientist is to combine domain expertise with statistical knowledge to help inform your analysis. You should thus provide concise answers to the following questions:
Task 3 (10 points)
There may be several more questions that you might need to answer to inform your analysis. Write the question, and the answer if you consider it necessary. Again, both questions and answers should be concise, as there will be an opportunity for more elaboration in the next tasks.
After having been exposed to machine learning techniques, you must provide a small essay-type report discussing what ML techniques could be applied to the previous dataset. (300 - 800 words)
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