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Unit outline_

MATH1115: Interrogating Data

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

In a data-rich world, global citizens need to problem solve with data, and evidence based decision-making is essential is every field of research and work. This unit equips you with foundational statistical thinking to interrogate data. Focusing on statistical literacy, the unit covers foundational statistical concepts such as visualising data, the linear regression model, and testing significance using the t and chi-square tests. Based on a flipped learning approach, you will experience most of your learning in weekly collaborative 2 hour labs, supplemented by readings and lectures. Working in teams, you will explore three real data stories across different domains, with associated literature. The combination of MATH1005 and MATH1115 is equivalent to DATA1001, allowing you to pathway to the Data Science, Statistics, or Quantitative Life Sciences majors.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 3
Prerequisites
? 
MATH1005 or MATH1015
Corequisites
? 
None
Prohibitions
? 
STAT1021 or ENVX1001 or ENVX1002 or BUSS1020 or ECMT1010 or DATA1001 or DATA1901
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Diana Warren, diana.warren@sydney.edu.au
Tutor(s) Martin Huang, martin.huang@sydney.edu.au
Raed Raffoul, raed.raffoul@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Practical exam
? 
Computer Prac Exam
For a given dataset with context, write a statistical report in RStudio.
60% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task LQuiz 3 (individual)
#earlyfeedbacktask
1% Week 03
Due date: 16 Aug 2024 at 23:59

Closing date: 16 Aug 2024
Due end of week 3.
Outcomes assessed: LO2 LO4 LO3
Presentation Project 1 report + presentation (group)
A data project, demonstrating ggplot, for own choice of data.
10% Week 05
Due date: 30 Aug 2024 at 23:59

Closing date: 09 Sep 2024
Self-directed learning till Week 5 Lab.
Outcomes assessed: LO1 LO6 LO3 LO2
Assignment Project 1 interrogation (group)
Code checking and review of another group's Project 1.
5% Week 07
Due date: 13 Sep 2024 at 23:59

Closing date: 23 Sep 2024
End of Week 7.
Outcomes assessed: LO1 LO6 LO3 LO2
Presentation Project 2 report + presentation (individual)
Data project, showing synthesis of course material, based on client data.
10% Week 10
Due date: 11 Oct 2024 at 23:59

Closing date: 21 Oct 2024
Self directed learning till Week 10 Lab.
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Assignment Project 2 interrogation (individual)
Code checking and review of another student's Project 2.
5% Week 12
Due date: 25 Oct 2024 at 23:59

Closing date: 04 Nov 2024
End of Week 12.
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Online task LQuiz (individual)
The LQuizzes allow weekly revision of RGuide and Course Material.
7% Weekly Due end of each week.
Outcomes assessed: LO2 LO5 LO4 LO3
Participation Labs (individual)
Participation in lab classes
2% Weekly 2 hrs
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

  • LQuizzes: The LQuizzes are designed to help you interact with the readings, in preparation for each lab. The LQuizzes will be held on the MATH1115 Canvas site. Each LQuiz consist of 5 randomised questions. They are worth 8%, and due at 23:59pm each Sunday night. Once started, they cannot be re-set. If you miss a quiz, it is not eligible for special consideration. Instead, the better mark principle will be used for the total marks on the Quizzes. This means that the total quiz mark counts if and only if it is better than or equal to 8% of your exam mark. If your total quiz mark is less than 8% of your exam mark, then 8% of your exam mark will be used instead. This allows you to improve in the exam.
  • Projects: The data projects are designed to develop your statistical literacy and computational ability. They must be submitted electronically as an HTML file via the MATH1115 Canvas site by the deadline. Late submissions will receive a penalty. They must be submitted electronically, via the DATA1001 Canvas site by the deadline. Late penalities apply. It is your responsibility to check that your project has been submitted correctly, otherwise it will not be marked.
    • Project 1 (group: both parts) is not eligible for special consideration, as it is a group task.
    • Project 2 (individual: both parts) is eligible for special consideration. You can be granted a simple extension, or if eligible, a longer extension up to a maximum of 10 days. If you do not submit by the approved deadline, you will need to re-apply for a special consideration. No submissions can be made more than 10 days past the original date - any further special considerations can only be granted a mark adjustment for the final exam.
  • Participation mark: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the labs. It is 0.25 marks per lab class up to 8 labs. You can apply for a special consideration - if eligible, it will be a mark adjustment for the final exam.

  • Examination: There is one practical examination of 1.5 hours duration during the examination period. The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Review Data Science & R Computer laboratory (2 hr) LO1 LO6
Week 02 Visualising data (ggplot) Computer laboratory (2 hr) LO3
Week 03 Data wrangling (tidyr & dyplyr) Computer laboratory (2 hr) LO2
Week 04 Data pipelining with Grammar of Graphics Computer laboratory (2 hr) LO2 LO3
Week 05 R Markdown report writing workshop; Presentation Computer laboratory (2 hr) LO1 LO2 LO3 LO6
Week 06 Linear models Computer laboratory (2 hr) LO4
Week 07 Regression tests and nonlinear models Computer laboratory (2 hr) LO4
Week 08 Hypothesis tests Computer laboratory (2 hr) LO5
Week 09 Binomial tests Computer laboratory (2 hr) LO5
Week 10 Revision + Presentation Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 11 Revision + Interrogation Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 12 Revision Computer laboratory (2 hr) LO2 LO3 LO4 LO5

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 3 credit point unit, this equates to roughly 60-75 hours of student effort in total.

Required readings

All material will be on Canvas.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. interrogate data in a team and communicate findings to diverse audiences through reproducible written and oral reports
  • LO2. explain the complexities of data wrangling
  • LO3. produce, interpret and compare graphical and numerical summaries, using ggplot
  • LO4. examine the relationships between variables using correlation and visualisation, and justify whether regression is an appropriate model for the data
  • LO5. formulate an appropriate hypothesis and perform a range, on a given real multivariate data and a problem, of hypothesis tests
  • LO6. investigate a real data story by researching associated literature, both in media and research journals.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

A tutorial participation mark was added; reduction in weekly quiz weightings and exam weighting.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.