DSB100 Fundamentals of 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: | DSB100 |
|---|---|
| Prerequisite(s): | (CAB201 or ITD121) and (MZB151 or any MXB unit). CAB201 can be studied in the same teaching period as DSB100. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $4,356 |
| International unit fee | $5,172 |
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | DSB100 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB201 or ITD121) and (MZB151 or any MXB unit). CAB201 can be studied at the same teaching period as DSB100. |
| Equivalent: | CAB220 |
| Coordinator: | David Lovell | david.lovell@qut.edu.au |
Overview
Data is becoming central to every organization's decision making process, and the demand for data savvy modelers and software engineers is rapidly increasing. Modern computational approaches to data analysis have to enable users to acquire, manage, interpret, present and disseminate large volumes of heterogeneous data. Data science is a synthesis of statistics, mathematics, machine learning and computer science, and uses tools, techniques, and approaches from all of these fields to extract information from datasets. This unit will introduce you to a wide range of Data Science methods and theories to model and analyze data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Define and scope data science questions, including determining which data are required to solve a problem
- Identify and use appropriate data acquisition, preparation, manipulation and modelling techniques relevant to the problem
- Critically apply exploratory data analysis, modelling and visualisation to analyse and understand data
- Identify and explain critical ethical issues in data science scenarios
- Create reproducible data analysis pipelines
Content
Data science is a synthesis of statistics, computer science and domain knowledge and uses tools, techniques, and approaches from all of these fields to extract insight from data. This can mean developing models to predict outcomes based on historical data, performing inference to test specific hypotheses, or transforming and manipulating large datasets to extract relevant information. With this in mind the content of this unit is designed to introduce students to some of the fundamental tools from these fields and how these tools might be used in conjunction to solve real world data science problems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode.You can on average expect to spend 10 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.The unit will be delivered through the following means:
- Pre-recorded lectures and other online activities, which provide the theoretical basis of the subject and provide formative feedback on your learning.
- Workshops which allow you to apply theory to practical, authentic data science problems. These workshops will be designed to reinforce key concepts and to assist in the completion of assessments.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- A range of formative exercises will be discussed during the learning activities;
- Comments and feedback on your portfolio submissions;
- Generic comments made to the cohort using QUT Canvas and other digital instruments;
You will receive written feedback on assessment tasks prior to the submission of the next assessment task. General feedback will be provided to the group.
Assessment
Overview
There are three assessments in this unit: involving two Problem Solving tasks and an examination. The Problem Solving tasks are hands-on activities designed for you to demonstrate a critical understanding of methods and techniques in Data Science. The practical examination allows you to demonstrate your understanding of the theories, methods and challenges in Data Science.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task 1
Analysis of a scenario and development of data science workflow, including interpretation and communication of results.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Problem Solving Task 2
You will be presented with a data set and asked to develop and implement methods to explore, analyse, visualise and model the data. You will be asked to identify and explain critical ethical issues.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Timed Online Assessment
A series of questions relating to the concepts in this unit.
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
No extraordinary charges or costs are associated with the requirements for this unit. Where applicable, online resources will be provided to the student as additional materials.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task 1, Problem Solving Task 2, Timed Online Assessment
Relates to: Problem Solving Task 1, Problem Solving Task 2, Timed Online Assessment
2: Engineering Application Ability
3: Professional and Personal Attributes
Unit Outline: Semester 2 2025, Online
| Unit code: | DSB100 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (CAB201 or ITD121) and (MZB151 or any MXB unit). CAB201 can be studied at the same teaching period as DSB100. |
| Equivalent: | CAB220 |
Overview
Data is becoming central to every organization's decision making process, and the demand for data savvy modelers and software engineers is rapidly increasing. Modern computational approaches to data analysis have to enable users to acquire, manage, interpret, present and disseminate large volumes of heterogeneous data. Data science is a synthesis of statistics, mathematics, machine learning and computer science, and uses tools, techniques, and approaches from all of these fields to extract information from datasets. This unit will introduce you to a wide range of Data Science methods and theories to model and analyze data.
Learning Outcomes
On successful completion of this unit you will be able to:
- Define and scope data science questions, including determining which data are required to solve a problem
- Identify and use appropriate data acquisition, preparation, manipulation and modelling techniques relevant to the problem
- Critically apply exploratory data analysis, modelling and visualisation to analyse and understand data
- Identify and explain critical ethical issues in data science scenarios
- Create reproducible data analysis pipelines
Content
Data science is a synthesis of statistics, computer science and domain knowledge and uses tools, techniques, and approaches from all of these fields to extract insight from data. This can mean developing models to predict outcomes based on historical data, performing inference to test specific hypotheses, or transforming and manipulating large datasets to extract relevant information. With this in mind the content of this unit is designed to introduce students to some of the fundamental tools from these fields and how these tools might be used in conjunction to solve real world data science problems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode.You can on average expect to spend 10 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.The unit will be delivered through the following means:
- Pre-recorded lectures and other online activities, which provide the theoretical basis of the subject and provide formative feedback on your learning.
- Workshops which allow you to apply theory to practical, authentic data science problems. These workshops will be designed to reinforce key concepts and to assist in the completion of assessments.
Feedback on Learning and Assessment
Feedback in this unit is provided to you in the following ways:
- A range of formative exercises will be discussed during the learning activities;
- Comments and feedback on your portfolio submissions;
- Generic comments made to the cohort using QUT Canvas and other digital instruments;
You will receive written feedback on assessment tasks prior to the submission of the next assessment task. General feedback will be provided to the group.
Assessment
Overview
There are three assessments in this unit: involving two Problem Solving tasks and an examination. The Problem Solving tasks are hands-on activities designed for you to demonstrate a critical understanding of methods and techniques in Data Science. The practical examination allows you to demonstrate your understanding of the theories, methods and challenges in Data Science.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task 1
Analysis of a scenario and development of data science workflow, including interpretation and communication of results.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Problem Solving Task 2
You will be presented with a data set and asked to develop and implement methods to explore, analyse, visualise and model the data. You will be asked to identify and explain critical ethical issues.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Timed Online Assessment
A series of questions relating to the concepts in this unit.
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
No extraordinary charges or costs are associated with the requirements for this unit. Where applicable, online resources will be provided to the student as additional materials.
Risk Assessment Statement
There are no unusual health or safety risks associated with this unit.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Problem Solving Task 1, Problem Solving Task 2, Timed Online Assessment
Relates to: Problem Solving Task 1, Problem Solving Task 2, Timed Online Assessment