讲解 BUSFIN 711、辅导 Python程序语言
BUSFIN 711 – FINANCIAL ANALYTICS APPLICATIONS
Assignment 3: Project
DUE: 4PM, FRIDAY 6 SEP 2024
General
• This is an individual assignment.
• The assignment is marked out of 100 marks and worth 40% of your overall grade
for this course.
• Please submit online through Canvas by the due date.
• In this course, students are prohibited from using generative artificial intelligence
text and art generation software, such as ChatGPT and DALL.E, to produce output
that is used directly as part of their assessments. The work you submit must be
substantially your own work, you must carry out your own analysis and write your
own text. You may use AI tools as a source of information or to help generate
ideas. However, you should take care to check the accuracy of information
provided by AI tools and where appropriate you should go back to the original
source of information.
• If you use external sources, you must provide references. This can either be done
in APA style or ‘professional style’ which should identify at a minimum the source,
author and date where applicable. You do not need to reference the lecture notes.
If you are referencing information obtained from AI tools, you should reference
the original source of the information, not the AI tool.
• Late submissions will lose 24% of marks for each day they are late.
Overall requirements
The purpose of this assignment is to write a manuscript with Quarto. You choose a
question you want to explore using one of the two datasets provided. To perform well on
this assignment, you should demonstrate a solid understanding of the Quarto manuscript
format and relevant Python techniques that have been covered in the course so far.
The two datasets you can choose from to create your manuscript and answer your
question are:
• Election data: P00000001-ALL.csv on https://github.com/wesm/pydatabook/tree/3rd-edition/datasets/fec
• Patent data: KPSS_2022.csv on https://github.com/KPSS2017/TechnologicalInnovation-Resource-Allocation-and-Growth-Extended-Data
Choose only one of these datasets. Do not use both of the datasets.
You should develop a question to explore and answer using the Quarto manuscript that
you will build based on your chosen dataset.
Detailed instructions
Submission format:
(1) Create an empty folder for Assignment 3.
(2) Create a Quarto manuscript project and store it in the folder created in Step 1.
Change the name of the index.qmd to 0_main_file.qmd.
(3) Develop your manuscript to meet requirements of reproducible analytical
pipelines and to produce a convincing manuscript.
(4) Compress the folder created in Step 1 and submit the compressed file to Canvas.
Meet requirements of reproducible analytical pipelines:
(1) Main content: The manuscript you create should be reproducible. Specifically,
numbers, figures, and tables should be traceable back to your code.
(2) Output format: HTML, Word, and PDF. You should make sure your manuscript
can be easily and automatically outputted as all these three formats.
Hint: Before submission, remember to click on Word and PDF within VS Code
preview to ensure these formats work properly.
(3) Article notebook (or qmd file)：Please ensure your article notebook appears
properly in the html format of the manuscript.
(4) Other notebooks (or qmd file): At least one additional notebook should appear in
the html format of the manuscript.
Hint: You can consider putting code related to figures into this notebook. Then
import figures generated from this script using Quarto external embeds.
Meet requirements of a convincing manuscript:
(1) Your story is convincing and gets good support from your codes, numbers,
figures, tables, equations, and descriptions.
(2) Your writing should be more than 1,000 words, but no more than 2,000
words.
(3) You should divide your writing into multiple sections. Any references should be
properly listed at the end of the manuscript.
(4) Your manuscript should demonstrate a good understanding of automatic crossreferences,
including figures, tables, equations, and sections.
(5) Please consider using relevant markdown techniques covered in the course to
make your manuscript more readable.