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代写辅导接单-MAST30034 Applied Data Science Project

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School of Mathematics and Statistics

Applied Data Science (MAST30034) 2026 An EndtoEnd Data Science Project

Due date: Monday 31th of August 11:59 pm Project Weight: 30%

Author: Akira Takihara Wang, Liam Hodgkinson, Mingming Gong

Project Overview

This project aims to make a quantitative analysis of the New York City Taxi and Limousine Service Trip Record Data. The dataset covers trips taken in various types of taxi and forhire vehicle services in the New York City area. The data in parquet format is directly downloadable from here, with corresponding usage guide linked here. You will need to choose a minimum of 6 months if working with PySpark or 3 months if working with pandas from 2016 or later (ensure your data includes Zones, not coordinates). PySpark is the expectation here; ensure you have obtained permission from your tutor to use pandas.

Students will be required to prepare a selfcontained report which must be 68 pages including figures, excluding references, and written using LaTeX. Please do not submit any other format written in Word or Google Docs, we are expecting a compiled pdf written in LaTeX. There are no exceptions.

Project Expectations

We understand that the page limit is strict and quite short. This project aims to get students to be able to concisely summarise information professionally.

Lastly, we know that the best way to learn new tools is to use and apply them in a project, consider this to be “the project”. Please try your best, the tutor team will be here to support you where possible. Sample solutions will be released at the start of Week 2.

Note: Students in prior years have often found themselves underestimating the time commitment required for this project. Be sure to start it ASAP; you should aim to have all of the results by the start of Week 4, so you can spend the remaining time writing.

Project Assumptions

Students are free to choose any software, language, or package that is deemed useful to complete this project, although it is strongly recommended that Python and PySpark be used.