Outline

Overview

The aim of this unit is to provide students with the knowledge and skills required to undertake moderate to high level data manipulation and management in preparation for statistical analysis of data typically arising in health and medical research. Specific objectives are for students to: gain experience in data manipulation and management using two major statistical software packages (Stata and R); learn how to display and summarise data using statistical software; become familiar with the checking and cleaning of data; learn how to link files through use of unique and non-unique identifiers; acquire fundamental programming skills for efficient use of software packages; and learn key principles of confidentiality and privacy in data storage, management and analysis. The topics covered are: Module 1 - Stata and R: The basics (importing and exporting data, recoding data, formatting data, labelling variable names and data values; using dates, data display and summary presentation); and creating programs. Module 2 - Stata and R: graphs, data management and statistical quality assurance methods (including advanced graphics to produce publication-quality graphs); Module 3 - Data management using Stata and R (using functions to generate new variables, appending, merging, transposing longitudinal data; programming skills for efficient and reproducible use of these packages, including loops and arguments.

Unit details and rules

Unit code	BSTA5004
Academic unit	Public Health
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Erin Cvejic, erin.cvejic@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment 1 Statistical computation tasks and report	30%	- Due date: 25 Aug 2022 at 23:59	6-8 pages
Outcomes assessed:
Assignment	Assignment 2 Statistical computation tasks and report	35%	- Due date: 22 Sep 2022 at 23:59	8- 10 pages
Outcomes assessed:
Assignment	Assignment 3 Statistical computation tasks and report	35%	- Due date: 27 Oct 2022 at 23:59	8-10 pages
Outcomes assessed:

Assessment summary

Assignment 1 will require you to complete several tasks using R and Stata, covering the content from Module 1
Assignment 2 will require you to complete several tasks using R and Stata, covering the content from Module 2
Assignment 3 will require you to complete several tasks using R and Stata, covering the content from Modules 1 – 3
Submission will be a written report which will usually require the inclusion of relevant software code and output

Detailed information for each assessment can be found on Canvas

Assessment criteria

Grade	Mark Range	Description
AF Absent fail	Range from 0 to 49	To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level.
FA Fail	Range from 0 to less than 50	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.
PS Pass	Range from 50 to less than 65	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard
CR Credit	Range from 65 to less than 75	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard
D Distinction	Range from 75 to less than 85	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard
HD High distinction	Range from 85 to 100 inclusive	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional 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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Module 1: The basics. Importing and exporting data; recoding and formatting data; labelling variables and values; use of date data, displaying and summarising data	Individual study (40 hr)
	Module 2: Graphs, Data management and Statistical Quality Assurance Methods. Includes advanced graphics for production of publication-quality graphs.	Individual study (40 hr)
	Module 3: Data Management. Using functions to generate new variables; appending, merging and transposing data; programming skills including loops, arguments and programs/macros.	Individual study (40 hr)

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 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

It is recommended that you have access to the following textbooks:

Jull S, Frydenberg M. An Introduction to Stata for Health Researchers, 4th ed. Stata Press, 2014.
Wickham H, Grolemund G. R for Data Science. O’Reilly 2017. Dalgaard, P. (available online https://r4ds.had.co.nz/)

The Wickham text is freely available at the web link provided. If you have any issues accessing these texts please contact the delivering unit coordinator.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. undertake data manipulation and management using two major statistical software packages (Stata and R)
LO2. appropriately display and summarise data using statistical software
LO3. understand how to check and clean data
LO4. link data files through unique and non-unique identifiers
LO5. have fundamental programming skills for efficient use of statistical software
LO6. understand key principles of confidentiality and privacy in data storage, management, and analysis

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
Learning outcomes

Responding to student feedback

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

DMC was last delivered in Semester 1 2022. In Semester 1, 2020, the R notes were redeveloped, and further changes were implemented including incorporation of online tutorials and video content. Further changes have been implemented in Semester 1, 2022, including additional modification of the course notes and creation of additional learning resources.

Additional information

This unit is delivered externally through the Biostatistics Collaboration of Australia (BCA).

Software requirements: This is a practical course which requires regular use of the relevant software. Access should be organised as soon as possible, as delays in gaining access may impact your ability to complete the course. You will require access to the following software packages:

Stata version 12 or later (the latest version is v17)
R version R64 3.4.2 or later (the latest version is 4.0.2)
RStudio version 1.3 or later (the latest version is 1.4)

Biostatistics Collaboration of Australia (BCA)

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BSTA5004: Data Management and Statistical Computing

Semester 2, 2022 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect