MXN500 Introduction to Statistics for Data Science

Unit Outline: Semester 1 2025, Gardens Point, Internal

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. The concepts in this unit are extended upon in MXN600.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Demonstrate an understanding of fundamental statistical models and methods.
2. Apply fundamental statistical modelling techniques in an appropriate way.
3. Use R, statistical software package, to model and analyse data.

Content

Learning Approaches

This unit involves 2 hours of lectures each week where theory and concepts will be presented and discussed, and where you will be exposed to the processes required to solve problems using the methods of this unit. There will also be 2 hours of practical computer lab activities each week.

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.

You are expected to work in any lecture/workshop session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented during class by working a wide variety of exercises, problems, and online learning activities in your own time.

Feedback on Learning and Assessment

Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.

Summative feedback will be provided throughout the semester with progressive posting of results via Canvas.

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.

This is an assignment for the purposes of an extension.

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 examination will require attendance on QUT campus.

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.

Requirements to Study

Costs

No extraordinary charges or costs are associated with the requirements for this unit.

Resources

Risk Assessment Statement

There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres. Emergency exits and assembly areas will be pointed out in the first few lectures. 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

EN55 Master of Professional Engineering

1. Apply advanced and specialist knowledge, concepts and practices in engineering design, analysis management and sustainability.
   Relates to: Workbook, Examination (invigilated)
2. Critically analyse and evaluate complex engineering problems to achieve research informed solutions.
   Relates to: Workbook, Examination (invigilated)
3. Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non specialist audiences.
   Relates to: Workbook

EN75 Master of Sustainable Infrastructure with Data Analytics

1. Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
   Relates to: Workbook, Examination (invigilated)
2. Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
   Relates to: Workbook, Examination (invigilated)
3. Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
   Relates to: Workbook, Examination (invigilated)

EN76 Master of Renewable Energy with Data Analytics

1. Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Renewable Energy and Data Analytics domains
   Relates to: Workbook, Examination (invigilated)
2. Analyse and evaluate problems in Renewable Energy and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
   Relates to: Workbook, Examination (invigilated)
3. Effectively communicate problems in Renewable Energy and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
   Relates to: Workbook, Examination (invigilated)

EN77 Master of Advanced Manufacturing with Data Analytics

1. Demonstrate and apply advanced and specialist theory-based discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Advanced Manufacturing and Data Analytics domains
   Relates to: Workbook, Examination (invigilated)
2. Employ advanced specialist technical skills, analysis approaches, design, and data to the solution of problems in Advanced Manufacturing and Data Analytics domains, critically evaluating solutions and practice-informed performance to deliver whole of life requirements and strategic objectives
   Relates to: Workbook, Examination (invigilated)
3. Implement professional communication and collaborative skills while engaging with stakeholders, exchanging ideas, and presenting complex information to specialist and non-specialist audiences in Advanced Manufacturing and Data Analytics domains
   Relates to: Workbook, Examination (invigilated)

IN20 Master of Information Technology

1. Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
   Relates to: ULO1, Workbook, Examination (invigilated)
2. 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)

IN30 Graduate Certificate in Data Science

1. Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
   Relates to: Workbook, Examination (invigilated)
2. Employ appropriate data science methods​ to derive insights from data to support decision-making.
   Relates to: Workbook, Examination (invigilated)
3. Apply problem solving approaches to design, execute and produce data science solutions.
   Relates to: Workbook, Examination (invigilated)

IN31 Master of Data Science

1. Demonstrate advanced knowledge of the principles, concepts, techniques, and approaches in data science.
   Relates to: Workbook, Examination (invigilated)
2. Skilfully use appropriate statistical, computational, and modelling techniques to derive insights from data to support decision-making.
   Relates to: Workbook, Examination (invigilated)
3. Critically apply specialist problem-solving approaches to design, execute and produce data science solutions.
   Relates to: Workbook, Examination (invigilated)