代写QBUS6860、代写Python语言程序

日期：2022-09-21 11:18

2022S2 QBUS6860 Individual Assignment Page 1 of 9

QBUS6860 – Individual Assignment:

Value: 45%

Due Date: 4pm Friday 23 September 2022

Rationale

This assignment has been designed to help students develop basic skills in data visualisation

and to allow students to practice techniques learned in lectures and tutorials.

Key Admin Information

1. Required submissions:

a. ONE written report (word or pdf format, through Canvas- Assignment 1

Report Submission).

b. SEVERAL Python “.py” or Jupyter Notebook “ipynb” files and any necessary

data files (through Canvas- Assignment 1 - Upload Your Program Code Files).

2. The late penalty for the assignment is 5% of the assigned mark per calendar day,

starting after 4pm on the due date 23 September 2022. Friday 30 September 2022,

4:00 pm is the closing date. Any submission later than the closing time/date will NOT

be accepted for marking.

3. Length: The main text of your report (including everything except for possible

appendices) should have a maximum of 10 pages in 12-point Times New Roman (or

Calibri) fonts and single line spacing, including all the plots, figures and tables (if any).

For each Task, you should write sufficient and complete information in the report with

necessary plots based on your visualisation, methodology, analysis, insight and

limitations, etc, when possible. The cover pages and appendices are NOT counted

towards the 10 page limit.

4. Numbers with decimals should be reported to the Two-decimal point in the report.

5. If you wish to include additional materials, you can do so by creating an appendix.

There is no page limit for the appendix. Keep in mind that making good use of your

audience’s time is an essential business skill. Extraneous and/or wrong material will

potentially affect your mark.

6. Anonymous marking: Given the anonymous marking policy of the University, please

only include your student ID (SID) in the submitted report, and do NOT include your

name. The file name of your report should follow the following format. Replace

"XXXX" with your SID in, for example, QBUS6860_2022S2_SIDXXXXX.pdf or

QBUS6860_2022S2_SIDXXXXX.docx. For your notebooks, please name them as

TaskA_SIDXXXX.ipynb and TaskB_SIDXXXX.ipynb respectively.

7. Presentation of the assignment is part of the assessment. Certain marks may be

deducted for low quality writing or lack of clarity in presentation.

8. For Turnitin to check your code, please copy and paste your codes into Appendix.

Code should be formatted by equal width fonts such as Courier New or Consola in 10-

point font size.

If your programs are in py file, simply copy and paste into the report Appendix. If you

are using Jupyter Notebook, please follow the instruction (InstructionPY.pdf on

2022S2 QBUS6860 Individual Assignment Page 2 of 9

Canvas) to convert it to “py” files first then copy the created py files into Appendix of

the report.

Missing code (Task A and/or Task B) in the appendix of the report will result in losing

50% awarded marks of Task A and/or Task B, respectively.

Key Rules

Carefully read the requirements for each part of the assignment.

Please follow any further instructions announced on Canvas. Failure to read

information and follow instructions may lead to loss of marks. Furthermore, note that

it is your responsibility to be informed of the University of Sydney and Business School

rules and guidelines, and follow them,

see https://canvas.sydney.edu.au/courses/9993/pages/submitting-assignments

You must use Python for the tasks of the assignment.

Reproducibility is fundamental in data analysis, so that you make sure you submit the

right Python py file(s) or Jupyter Notebook ipynb files that generate the results in your

report. Markers will run your program for checking.

The University of Sydney takes plagiarism very SERIOUSLY. Please be warned that

plagiarism between individuals/groups is always obvious to the markers and can be

easily detected by Turnitin.

Not submitting your code will lead to a loss of 50% of the awarded marks.

Referencing: Business School recommends APA Referencing System. (You may find

the details at: https://libguides.library.usyd.edu.au/citation/apa7 )

Feedback will be provided on the marked submission.

Warning: Your submission time will be the time of the last submission of the Two

components (the report and the code) to Canvas. For example, if any one of two

components is submitted later than due time/day, the entire submission of your

assignment will be regarded as a late submission and will be subject to a late penalty

accordingly. If you want to re-submit any missing items/components after the official due

date has passed, you will receive the late penalty.

Task A (40 Marks)

This task is designed for you to practice your skills in managing data and conducting basic

Visual Data Analytics (VDA) and Exploratory Data Analysis (EDA).

Background

Formula 1 is the highest class of single-seater auto racing regulated by the Fédération

Internationale de l'Automobile (FIA) and owned by the Formula One Group. The FIA Formula

One World Championship has been one of the most innovative forms of racing around the

world since its first season in 1950. The word "formula" in the name refers to the set of rules

to which all participants' cars must conform. A Formula One season consists of a series of

races, known as Grands Prix, which take place worldwide on purpose-built circuits and on

public roads. You may do a quick research on https://en.wikipedia.org/wiki/Formula_One

2022S2 QBUS6860 Individual Assignment Page 3 of 9

to understand its rules and points system. This information is helpful for you to complete this

task.

Resources

https://www.formula1.com/en/results.html collects all the F1 races results for all the years.

For example, you can click 2021 in the above page to go to the 2021 result page from which

you can get access to the information of races, drivers and teams etc.

You may re-use part of lecture or tutorial codes and revise it for your purpose here.

Subtasks

You shall focus on all the races in 2021, see

https://www.formula1.com/en/results.html/2021/races.html.

1. First use python code to get the table in

https://www.formula1.com/en/results.html/2021/races.html showing the winner

and the winning time for each of 22 races between 28 March 2021 and 12 December

2021. Draw a bar chart to show the winning time for all the 22 races in the year.

Describe how you make a more informative plot and the reason why you do this in

your report.

2. From the table in the above page, the first column has URL links to race result. For

example, the first URL is

https://www.formula1.com/en/results.html/2021/races/1064/bahrain/race-

result.html

Either manually or automatically (using python code) get all the URLs for 22 races of

the year. Report at least three URLs and describe these races such as time and location

etc in your report. If you manually obtain the URLs (i.e. looking through the webpage

to read out/get them one by one) without using python code, please describe how

this can be done with a scraping method.

3. On the left panel of the race-result page, there is a link to FAST LAPS information. The

fast lap page URL can be obtained by replacing race-result.html with fastest-

laps.html. Write your own python code in your notebook to scrape all the Fastest-

laps results for 2021 from each URL of 22 fastest-laps.html (all the URLs can be

easily formed by replacing race-result.html with fastest-laps.html in the

above subtask 2). The table in fastest laps page contains information such as Position,

Driver No, Driver Names, Cars, Laps, Time of Day, Time and Average (AVG) Speed.

Draw a plot to show the average speed for each of the drivers in 22 races. Here the

average speed of a driver is the average value of the driver’s 22 AVG Speeds on the

fastest lap. In your report, describe the information you have gathered and explain

your methods with some key code snippets if necessary.

2022S2 QBUS6860 Individual Assignment Page 4 of 9

4. The point information from 22 race results tables has been merged for you in a csv file

(season_results2021.csv) in which the columns contain the points earned by

drivers in each race with locations as column names in additional to driver names

column and the car name column. Use python to draw a nice line plot to show the

cumulative points (as the y-axis) of all the drivers against the races (as the x-axis) from

28 March 2021 and 12 December 2021 (i.e., the order of days that the races take

place). In your report, explain your methods and all the features used (like colors,

legends etc) and present your visual product along with necessary description and

explanation.

Always keep in mind the visual presentation should be meaningful and visually

pleasing.

5. Conduct appropriate analysis, summarise and report your insights/conclusion.

Task B (60 Marks)

This task is designed for you to practice data-based storytelling by conducting basic Visual

Data Analytics (VDA) and Exploratory Data Analysis (EDA).

Background

Gapminder Foundation https://www.gapminder.org is a non-profit venture registered in

Stockholm, Sweden, that promotes sustainable global development and achievement of the

United Nations Millennium Development Goals by increased use and understanding of

statistics and other information about social, economic and environmental development at

local, national and global levels.

Resources

You will be given the following datasets which can be downloaded from

https://www.gapminder.org/data/ via, for example, Select an indicator>Economy.

1. life_expectancy_years.csv: Health > Life expectancy

2. gdppercapita_us_inflation_adjusted.csv: Economy>Income & Growth >

GDP / Capital (US$ inflation_adjusted)

3. hdi_human_development_index.csv: Society > Human Development Index

4. income_per_person_gdppercapita_ppp_inflation_adjusted.csv:

Economy > Income & Growth > Income

5. total_health_spending_percent_of_gdp.csv: Health > Health Economy >

Total Health spending (% of GDP)

6. world_regions.csv

The Task

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The main objective is to enhance our understanding regarding the relationship among the

metrics of GDP growth aligned with Health spending, Income, Human Development Index

(HDI) and Life Expectancy of different countries by visual data analytics.

1. Conduct a thorough analysis over all the datasets, report and explain all the key

statistics. Do the necessary clean-up for visualisation.

2. Choose two appropriate visualisation types (or more) to answer the following

questions or reveal the visual facts as the answer to the following questions. (i) What

is the effect of GDP on other metrics? (ii) How are those metrics distributed for

different countries or regions? For the second question, you can take a strategy by

choosing one country from Africa, one country from Europe, and one country from

Asia to compare to Australia (or a small group of countries for each region). A region

data file has been provided for your convenience. In your report, please also describe

how to produce your plots.

3. Summarise your findings, observations, insights and conclusions etc based on the

visualisation analysis in your report.

Note:

1. To have a better story with more thorough analysis, you may explore more data from

gapminder in addition to the given dataset (you must analyse the given datasets). If

you have used extra datasets from gapminder, please submit your own dataset with

your notebook programs.

2. Your program must be done in Python. The code used for analysis and visualisation

should be well presented in a separate notebook with sufficient comments so that the

reader can easily understand your methods.

3. You shall regard this as your own small research project. Think carefully and do it

thoroughly. Do not expect you can complete this in one day, so you need to plan it

earlier. You may refer to a sample project at https://github.com/jmlcode/p1-

investigate-datasets.

2022S2 QBUS6860 Individual Assignment Page 6 of 9