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Unit outline_

MATH1115: Interrogating Data

Intensive January, 2023 [Block mode] - Remote

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

No

Teaching staff

Coordinator Daniel Hauer, daniel.hauer@sydney.edu.au
Lecturer(s) Emily Cooper, e.cooper@sydney.edu.au
Type Description Weight Due Length
Assignment LQuiz
The LQuizzes allow daily revision of RGuide and Course Material.
10% Progressive Three per week, in total twelve.
Outcomes assessed: LO2 LO3 LO4 LO5
Presentation Project 1 report + presentation
A data project, demonstrating ggplot, for own choice of data.
10% Week 02
Due date: 22 Jan 2023 at 23:59

Closing date: 29 Jan 2023
Self-directed learning.
Outcomes assessed: LO1 LO6 LO3 LO2
Assignment Project 1 Interrogation
Code-checking and review of another group's Project 1.
5% Week 03
Due date: 29 Jan 2023 at 23:59

Closing date: 05 Feb 2023
After the Project 1 presentations.
Outcomes assessed: LO1 LO2 LO3 LO6
Presentation Project 2 report + presentation
A data project showing synthesis of course material, based on client data.
10% Week 04
Due date: 05 Feb 2023 at 23:59

Closing date: 12 Feb 2023
Self-directed learning.
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Assignment Project 2 Interrogation
Code checking and review of another student's Project 2.
5% Week 05
Due date: 12 Feb 2023 at 23:59

Closing date: 15 Feb 2023
After Project 2 presentations.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Monitored exam
? 
Computer Prac Exam
MC and for a given dataset with context, write a statistical report in R.
60% Week 06
Due date: 16 Feb 2023 at 09:00
1.5 hours
Outcomes assessed: LO2 LO3 LO4 LO5 LO6

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. There are 12 LQuizzes to attempt, three per week, but only the best 10 LQuizzes count towards the final mark. The better mark principle will be used for the total marks on the LQuizzes. Thus, you dont need to submit an application for Special Consideration or Special Arrangements if you miss a quiz! The better mark principle means that the total quiz mark counts if and only if it is better than or equal to your exam mark. If your total quiz mark is less than your exam mark, the exam mark will be used for that portion of your assessment instead.
  • 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.
  • Examination: There is one prac examination of 1.5 hours’ duration during the examination period.

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 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.

WK Topic Learning activity Learning outcomes
Progressive Review Data Science + R Computer laboratory (2 hr) LO1 LO6
Data visualisation 1 (ggplot) Computer laboratory (2 hr) LO3
Data wrangling (tidyr & dyplyr) Computer laboratory (2 hr) LO2
Data visualisation 2 (more advanced ggplot) Computer laboratory (2 hr) LO2 LO3
Presentation Computer laboratory (2 hr) LO1 LO2 LO3 LO6
Linea regression 1 Computer laboratory (2 hr) LO4
Linear regression 2 Computer laboratory (2 hr) LO4
Regression Tests & Chi-squared Tests Computer laboratory (2 hr) LO5
Binomial Formula Computer laboratory (2 hr) LO5
Presentation Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Revision ( + Interrogation) Computer laboratory (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Revision Computer laboratory (2 hr) LO2 LO3 LO4 LO5

Attendance and class requirements

Classes will be available in a ‘Hyflex’ delivery mode – ie the classes will be on campus (CC), with a Zoom link for those studying remotely (RE).

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.

No changes have been made since this unit was last offered.

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.