Skip to main content
Unit outline_

OLET5606: Data Wrangling

Intensive June - July, 2020 [Block mode] - Camperdown/Darlington, Sydney

Data comes in many and varied formats, it can be tall or wide, big or small, structured or unstructured. Regardless of where you get your data from, it will almost always require some wrangling. Data wrangling is the convolution, alignment and preparation of data before use. This unit provides an overview of best practices in organising your research data from the point of discovery through to its use for scientific applications. You will learn the principles of data handling and how to maintain rigour and integrity of your data throughout your research, including documenting data provenance, how to access major databases, and data licensing. After calculating summary statistics to aid in the identification of outliers and missing values, you will learn how to clean and wrangle data in a reproducible manner in R, at a variety of scales. You will "wrangle" your research data using R, identifying outliers and missing values and ensuring provenance.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 2
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Basic exploratory data analysis, basic coding in R

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Diana Warren, diana.warren@sydney.edu.au
Lecturer(s) Diana Warren, diana.warren@sydney.edu.au
Type Description Weight Due Length
Assignment hurdle task Live Lab Report + Presentation
Reproducible report (Rmd) + Presentation
70% Ongoing Self paced, due before & in LiveLab3
Outcomes assessed: LO4
Assignment hurdle task Mastery Quizzes
Canvas Quiz
30% Progressive Review of Modules 0,1,2
Outcomes assessed: LO1 LO2 LO3
hurdle task = hurdle task ?

Assessment summary

  • Mastery Quizzes – These are a hurdle task that need to be completed before Live Lab1. Hence, no late penalties apply.
  • Live Lab Report + Presentation – These are designed to all be completed by and in Live Lab 3. In special circumstances, special consideration can be applied for, or a 5% penalty per day applies. However, the interrogation aspect of the marks is forfeited if Live Lab 3 is missed, as it must be assessed concurrently with the other students’ reports.

 

 

Assessment criteria

Result code

Result name

Mark range

Description

HD

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard.

DI

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard.

CR

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard.

PS

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard.

FA

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
Ongoing Modules 0,1,2 Independent study (10 hr) LO1 LO2 LO3
Module 3 Block teaching (6 hr) LO4

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 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Required readings

Optional Reading: Data Wrangling with R (Boehmke, B, 2016)

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. Describe the importance of data provenance, and major databases that can be used to mine data.
  • LO2. Define data licensing.
  • LO3. Calculate summary statistics to identify outliers and missing values.
  • LO4. Clean and wrangle data in a reproducible manner in R, at a variety of scales.

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.

This is the first time this unit has been offered

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.