MXQ500 Introduction to Statistics for Data Science
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXQ500 |
|---|---|
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $592 |
| Domestic tuition unit fee | $3,900 |
| International unit fee | $5,220 |
Unit Outline: Session 1 2026, QUT Online, Online
| Unit code: | MXQ500 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXN500 |
Overview
Statistics forms the foundation of many tools and techniques used in data analytics. Therefore, appropriate application of statistical methods is essential in many quantitative roles and data science applications. The focus of this unit is on applying statistical methods in real-world contexts. You will look for meaningful patterns and model data to increasing levels of complexity. We will cover data and variables, visualisation, introductory probability, hypothesis testing, and linear regression. You will also learn how to select and apply appropriate quantitative methods using software such as R, an open-source statistical software. You will practice your quantitative skills using real data from scientists, business, and governments. This unit is appropriate for those requiring an introduction to, or a refresher in, statistics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate an understanding of fundamental statistical models and methods.
- Apply fundamental statistical modelling techniques in an appropriate way.
- Use R, statistical software package, to model and analyse data.
Content
The content of this unit includes:
- Introduction to programming using the R statistical software package.
- Fundamental types of data.
- Explorative data analysis and visualisation.
- Introduction to probability, random variables and distributions.
- Statistical significance and hypothesis testing.
- Linear regression.
- Parameter estimation and statistical inference.
All data analytics will be performed using the R statistical software package.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
The material presented will be context-based utilising examples from a range of real-world applications. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Appropriate approaches to the communication of mathematical and statistical information to diverse audiences will be explored via the examples presented in lectures and workshops.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
Formative assessment will be conducted throughout the semester in the practical classes. There will be progressive summative assessment related to the general topics covered in this unit, followed by a final examination. All assessment items will focus on the key concepts and methodologies taught in this unit with a required level of effective written communication.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Workbook
You will be required to complete problem solving exercises where you will apply your operational knowledge and problem solving skills in analysing real world datasets. The tasks will reflect the material taught throughout the unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment will consist of questions relating to material taught throughout the whole semester, with an emphasis on using techniques and interpreting output and results in context.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no set texts for this unit.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
Moore, D.S, McCabe, G.P. and Craig, B.A. (2009). Introduction to the Practice of Statistics. 6th Edition. W.H. Freeman and Company.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, Workbook, Examination (invigilated) - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO2, ULO3, Workbook, Examination (invigilated)
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Workbook, Examination (invigilated) - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, ULO3, Workbook, Examination (invigilated)
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, Workbook, Examination (invigilated) - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, Workbook, Examination (invigilated) - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Workbook, Examination (invigilated)
Unit Outline: Session 3 2026, QUT Online, Online
| Unit code: | MXQ500 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXN500 |
Overview
Statistics forms the foundation of many tools and techniques used in data analytics. Therefore, appropriate application of statistical methods is essential in many quantitative roles and data science applications. The focus of this unit is on applying statistical methods in real-world contexts. You will look for meaningful patterns and model data to increasing levels of complexity. We will cover data and variables, visualisation, introductory probability, hypothesis testing, and linear regression. You will also learn how to select and apply appropriate quantitative methods using software such as R, an open-source statistical software. You will practice your quantitative skills using real data from scientists, business, and governments. This unit is appropriate for those requiring an introduction to, or a refresher in, statistics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate an understanding of fundamental statistical models and methods.
- Apply fundamental statistical modelling techniques in an appropriate way.
- Use R, statistical software package, to model and analyse data.
Content
The content of this unit includes:
- Introduction to programming using the R statistical software package.
- Fundamental types of data.
- Explorative data analysis and visualisation.
- Introduction to probability, random variables and distributions.
- Statistical significance and hypothesis testing.
- Linear regression.
- Parameter estimation and statistical inference.
All data analytics will be performed using the R statistical software package.
Learning Approaches
This unit is designed for asynchronous online study, with activities including numerous short videos, podcasts and exercises carefully chosen to reinforce key skills and concepts. Students will have the opportunity to participate in online discussions with peers and teaching staff.
The material presented will be context-based utilising examples from a range of real-world applications. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Appropriate approaches to the communication of mathematical and statistical information to diverse audiences will be explored via the examples presented in lectures and workshops.
Feedback on Learning and Assessment
There are multiple ways for you to receive feedback on your learning and progress in this unit. These include:
- formative in-class individual and whole-of-class feedback provided by unit staff during discussion activities
- responses to questions posed through the unit communication channel from your peers and teaching staff
- feedback given on your assessment items individually via the rubric and written feedback.
Assessment
Overview
Formative assessment will be conducted throughout the semester in the practical classes. There will be progressive summative assessment related to the general topics covered in this unit, followed by a final examination. All assessment items will focus on the key concepts and methodologies taught in this unit with a required level of effective written communication.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Workbook
You will be required to complete problem solving exercises where you will apply your operational knowledge and problem solving skills in analysing real world datasets. The tasks will reflect the material taught throughout the unit.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment will consist of questions relating to material taught throughout the whole semester, with an emphasis on using techniques and interpreting output and results in context.
The use of generative artificial intelligence (GenAI) tools is prohibited during this assessment.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
There are no set texts for this unit.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
Moore, D.S, McCabe, G.P. and Craig, B.A. (2009). Introduction to the Practice of Statistics. 6th Edition. W.H. Freeman and Company.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IQ19 Graduate Diploma in Information Technology
- Demonstrate advanced IT knowledge in one or more IT disciplines.
Relates to: ULO1, Workbook, Examination (invigilated) - Apply advanced, industry-best practice, IT methods, tools and techniques to develop and implement IT systems, processes and/or software.
Relates to: ULO2, ULO3, Workbook, Examination (invigilated)
IQ20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline
Relates to: ULO1, Workbook, Examination (invigilated) - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, ULO3, Workbook, Examination (invigilated)
IQ30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: ULO1, Workbook, Examination (invigilated) - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: ULO2, Workbook, Examination (invigilated) - Apply problem solving approaches to design, execute and produce data science solutions.
Relates to: ULO3, Workbook, Examination (invigilated)