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Unit outline_

BSTA5004: Data Management and Stats Computing (DMC)

Semester 2, 2023 [Online] - Camperdown/Darlington, Sydney

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. Students will 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.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
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 2
Statistical computation tasks and report
35% Mid-semester break
Due date: 25 Sep 2023 at 23:59
Approximately 8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO5
Assignment Assignment 3
Statistical computation tasks and report
35% STUVAC
Due date: 06 Nov 2023 at 23:59
Approximately 8-10 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Assignment 1
Statistical computation tasks and report
30% Week 05
Due date: 28 Aug 2023 at 23:59
Approximately 6-8 pages
Outcomes assessed: LO1 LO3 LO5

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.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 10 calendar days (including weekends and public holidays).

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
Multiple weeks Module 1: Importing and exporting data; recoding and formatting data; labelling variables and values; use of date data, displaying and summarising data. Construction of suitable programming scripts to reproduce results. Independent study (40 hr) LO1 LO2 LO3
Module 2: Graphs, Data management and Statistical Quality Assurance Methods. Includes advanced graphics for production of publication-quality graphs. Individual study (40 hr) LO1 LO2 LO3 LO4
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) LO1 LO3 LO4 LO5 LO6

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, 5th ed. Stata Press, 2021.
  • Wickham H, Cetinkaya-Rundel M, Grolemund G. R for Data Science (2nd ed.).. O’Reilly 2023. (available online https://r4ds.hadley.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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

DMC was last delivered in Semester 1 2023. References for the sections in the new edition of the R textbook was updated. Otherwise, no major unit changes were implemented.

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 14 or later (the latest version is v18)
  • R version R64 4.02 or later  (the latest version is 4.3.1)
  • RStudio / IDE version 1.4 or later

Required mathematical background: Mathematical proficiency at a pre-university level (basic algebra and statistics, such as familiarity with percentiles, interquartile ranges, standard deviation, mean and median) is assumed.

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.