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
2022S2 QBUS6860 Individual Assignment Page 5 of 9
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