Outline

Overview

This unit provides a practical framework for understanding and applying data science in the built environment. Students will learn the fundamentals of building control systems and also develop skillsets to collect, analyse, and interpret data from various sources such as building systems, sensors, and human responses. The focus is on practical skills, including the use of data analysis tools to parse datasets and extract actionable insights that can lead to enhanced building performance. The unit will also cover the ethical considerations and data governance challenges present in the building sector. By the end of the unit, students will be able to apply data science methods to real-world problems in the context of building operation and intelligent control systems.

Unit details and rules

Academic unit	Architecture
Credit points	6
Prerequisites ?	None
Corequisites ?	BPSD5040
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Chirag Deb, chirag.deb@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Data handling To develop a report encompassing data importing, data handling and description.	40%	Week 07 Due date: 16 Sep 2025 at 23:59	12-page report including code.	AI allowed
Outcomes assessed:
Research analysis	Data analysis and modelling To develop a report which encompasses time series analysis, modelling, synthesis, etc.	60%	Week 13 Due date: 07 Nov 2025 at 23:59	15-page report including code.	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

Assessment details can be found on canvas.

Students should be present and engaged in their learning during classes. Late arrival/early departure will be deemed as an absence. Students who do not meet the minimum 90% threshold, who have reasonable evidence to support their absence, may be offered the opportunity to sit an alternative assessment to pass this unit at the discretion of the unit coordinator.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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:

In accordance with University of Sydney School of Architecture Design and Planning Faculty Resolutions 2022, for every calendar day up to and including 10 calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to the late work. For work submitted more than 10 calendar days after the due date, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Unit; Introduction to data in the built environment; Introduction to Assignment 1	Lecture (2 hr)
	Introduction to programming in Python.	Practical (2.5 hr)
	Introduction to programming in Python.	Tutorial (0.5 hr)
Week 02	Types of building data formats, size, storage.	Lecture (2 hr)
	Data handling.	Practical (2.5 hr)
	Data handling.	Tutorial (0.5 hr)
Week 06	Energy and IEQ sensing technology; Data transmission; Communication protocols.	Lecture (2 hr)
	Data visualisation.	Practical (2.5 hr)
	Data visualisation.	Tutorial (0.5 hr)
Week 08	Intelligent building management systems: Privacy and security.	Lecture (2 hr)
	Introduction to libraries in Python.	Practical (2.5 hr)
	Introduction to libraries in Python.	Tutorial (0.5 hr)
Week 10	Building automation and data-driven modelling of building systems.	Lecture (2 hr)
	Dataframes and analysis techniques.	Practical (2.5 hr)
	Dataframes and analysis techniques.	Tutorial (0.5 hr)
Week 11	Case studies and analysis with real world data.	Lecture (1 hr)
	Data modelling and prediction.	Practical (3.5 hr)
	Data modelling and prediction.	Tutorial (0.5 hr)
Week 13	Case studies and analysis with real world data.	Lecture (1 hr)
	Development of advanced prediction models.	Practical (3.5 hr)
	Development of advanced prediction models.	Tutorial (0.5 hr)

Attendance and class requirements

Please refer to the Resolutions of the University School: University of Sydney School of Architecture Design and Planning Faculty Resolutions 2022. Clause 8 (3) (a). Students are expected to attend a minimum of 90% of timetabled activities for each unit of study, unless granted exemption.

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

Artificial Intelligence: A Modern Approach, 4th US ed. by Stuart Russell and Peter Norvig
Generative AI Foundations in Python Carlos Rodriguez 2024
Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, Dilip Gudivada2023
An Introduction to Statistical Learning with Applications in Python Authors: Gareth James , Daniela Witten , Trevor Hastie , Robert Tibshirani , Jonathan Taylor

The University of Sydney library has either physical or digital copies of these books. Please contact the unit coordinator if you have difficulties accessing these.

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. Develop an understanding of the techniques and ethics of data science as applied to the built environment.
LO2. Apply data analysis tools to datasets from building systems, sensors, and human response data.
LO3. Extract insights from trends, patterns, and anomalies to optimise building performance, energy efficiency, and occupant comfort.
LO4. Synthesise information to inform decision-making processes in the built environment.
LO5. Critically examine the implications of datasets from the built environment for data governance.

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.

This is the first time this unit is being offered.

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

BPSD5050: Data Science for the Built Environment

Semester 2, 2025 [Block mode] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links