代写LUBS5996M Understanding Data for Decision Making Semester 2 – 2023/24代写C/C++语言
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Assessed Coursework
(Semester 2 – 2023/24)
100% Assignment
This assignment has been designed to enable you to evidence your ability to prepare, understand, and analyse data for informed decision making in a managerial capacity. The coursework will be assessed against the criteria mentioned at the end of this brief.
You are a manager in an airline company, AirGlide, which is planning to enter into European market. AirGlide has operated within North America, with several destinations. They have a track record of being in the top 5 most punctual North American airlines.
AirGlide has planned to set its operational base in the UK and to start several routes across Europe from that base. We assume that they have deep pockets to cover the costs and achieve break-even in no more than four years. You can make additional assumptions on your own as long as they are logical and consistent with the information provided in this brief, and are clearly articulated in your submitted report.
The big question to investigate is:
• Which airport in the UK would be most suited as the operational base?
You are provided with an open dataset containing information on all flights in and out of the UK from 2018 to 2023. As a part of this assessment, it is expected that students will identify other data sources that can help improve the decision-making process. As an example, an open access data on UK weather stations is provided that might help analysing the performance of different airports under different weather conditions. In order to answer this big question, you must demonstrate your skills in the following areas:
• Data Understanding: Inspect the data provided with the case study. To demonstrate your critical thinking, identify and suggest additional sources of data that are not provided but can help investigate this big question.
• Data Preparation: Performing operations like filtering, sorting, transformation, and enrichment to make data more useful and information-rich before analysing them.
• Exploratory Analysis: Calculate basic statistics, create visualizations (charts, graphs) to represent key trends, seasonal variations, and to identify any outliers.
• Managerial Insights: Provide insights derived from your analysis. For example, structured analysis of available airports and their feasibility scores for different criteria (e.g. structured decision tables, cost-benefit analysis etc.)
• Recommendations: Formulate actionable recommendations for setting up an operational base in the UK.
Assignments should not exceed 3,000 words in length
All coursework assignments that contribute to the assessment of a module are subject to a word limit, as specified in the online module handbook in the relevant module area of the MINERVA. The word limit is an extremely important aspect of good academic practice, and must be adhered to. Unless stated specifically otherwise in the relevant module handbook, the word count includes EVERYTHING (i.e. all text in the main body of the assignment including summaries, subtitles, contents pages, tables, supportive material whether in footnotes or in-text references) except the main title, reference list and/or bibliography and any appendices. It is not acceptable to present matters of substance, which should be included in the main body of the text, in the appendices (“appendix abuse”). It is not acceptable to attempt to hide words in graphs and diagrams; only text which is strictly necessary should be included in graphs and diagrams.
You are required to adhere to the word limit specified and state an accurate word count on the cover page of your assignment brief. Your declared word count must be accurate, and should not mislead. Making a fraudulent statement concerning the work submitted for assessment could be considered academic malpractice and investigated as such. If the amount of work submitted is higher than that specified by the word limit or that declared on your word count, this may be reflected in the mark awarded and noted through individual feedback given to you.
The deadline date for this assignment is 12:00:00 noon on Wednesday 15th May 2024.
An electronic copy of the assignment must be submitted to the Assignment Submission area within the module resource on the Blackboard MINERVA website no later than 12:00:00 noon prompt on the deadline date.
Faxed, emailed or hard copies of the assignment will not be accepted.
Failure to meet this initial deadline will result in a reduction of marks, details of which can be found at the following place:
https://lubswww.leeds.ac.uk/TSG/coursework/
SUBMISSION
Please ensure that you leave sufficient time to complete the online submission process, as upload times can vary. Accessing the submission link before the deadline does NOT constitute completion of submission. You MUST click the ‘CONFIRM’ button before 12:00:00 noon for your assignment to be classed as submitted on time, if not you will need to submit to the Late Area and your assignment will be marked as late. It is your responsibility to ensure you upload the correct file to the MINERVA, and that it has uploaded successfully.
It is important that any file submitted follows the conventions stated below:
FILE NAME
The name of the file that you upload must be your student ID only.
ASSIGNMENT TITLE
During the submission process the system will ask you to enter the title of your submission. This should also be your student ID only.
FRONT COVER
The first page of your assignment should always be the Assessed Coursework Coversheet (individual), which is available to download from the following location:
https://students.business.leeds.ac.uk/forms-guidance-and-coversheets/
STUDENT NAME
You should NOT include your name anywhere on your assignment