Outline

Overview

This unit covers computation and data handling, integrating sophisticated use of existing productivity software, e. g. spreadsheets, with the development of custom software using the general-purpose Python language. It will focus on skills directly applicable to data-driven decision-making. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model. This unit includes the content of DATA1002, along with additional topics that are more sophisticated, suited for students with high academic achievement.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	INFO1903 or DATA1002
Assumed knowledge ?	This unit is intended for students with ATAR at least sufficient for entry to the BSc/BAdvStudies(Advanced) stream, or for those who gained Distinction results or better, in some unit in Data Science, Mathematics, or Computer Science. Students with portfolio of high-quality relevant prior work can also be admitted
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Tongliang Liu, tongliang.liu@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 exam ?	Exam CLOSED book exam with MC and short essay questions.	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Weekly revision quizzes Answer short essay questions. w2-3 serves as Early Feedback	10%	Multiple weeks	N/A	AI allowed
Outcomes assessed:
Practical skill	Weekly coding tasks Python & shell coding to meet automated tests w1-4 serves as Early Feedback #earlyfeedbacktask	5%	Multiple weeks	N/A	AI allowed
Outcomes assessed:
Data analysis	Project stage 1 Report describes dataset, metadata, and how data was cleaned and ingested.	5%	Week 06	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Practice Python coding test Python coding to pass automated tests	0%	Week 09	-	AI prohibited
Outcomes assessed:
Data analysis	Project stage 2 Report: data summaries and charts, describe computation, evaluation.	10%	Week 09	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Python coding test Python coding to pass automated tests (CLOSED book) in the lab	10%	Week 11	90 minutes	AI prohibited
Outcomes assessed:
Data analysis	Project stage 3 Report with predictive model and evaluation of its success.	5%	Week 12	n/a	AI allowed
Outcomes assessed:
Data analysis	Project stage 4 (advanced) Produce interactive visualisation of data	5%	Week 13	n/a	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Early feedback task: A simple MCQ covering the first 3 weeks of content to help students aware of the progress and knowledge understanding, so that students can make plan and decision to study this unit of study.
- Late work and simple extension are not accepted for these assessments.
- When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
Weekly coding tasks: Tasks where the student must write a Python program to produce precisely described output. The program will be run automatically against test cases. Grade is based on participation. These are started in tutorial, and completed in students’ own time.
- Late work and simple extensionare not accepted for these assessments.
- When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
Weekly revision quiz: Each quiz consists of essay questions related to the lecture, lab, or tutorial content from the previous week. Done in students own time but need to submit by end of the week.
- Late work and simple extensionare not accepted for these assessments.
- When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
Practice Python coding test: Done in student’s own time. There are several tasks in the test. For some of tasks, the student must extend a Python code skeleton, for others, the student must develop the code from scratch. The aims are the same, i.e. to calculate precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test, and to trigger remedial learning for any student who does not succeed in the practice.
- Late work is not accepted for this assessment.
- When special consideration is approved, the appropriate consideration should be “No action”.
Python coding test: Held during your lab session. There are several tasks in the test. For some of tasks, the student must extend a Python code skeleton, for others, the student must develop the code from scratch. The aims are the same, i.e. to calculate precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described.
- Late work and simple extensionare not accepted for these assessments.
- When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” where this 10% will be added to your exam, i.e. your exam will carry 60% of the total mark.
Project Stage 1: This is the first part of a group project (the students in a group must all be attending the same scheduled lab session). This stage involves finding data from a domain of interest for the students, data cleaning and importing to a tool, and doing a very simple analysis from some of the data. A report is required that describes the dataset (including the metadata associated to it), how it was obtained, and how it was processed by the tool. If this stage is missed or badly done, the group can be given a clean data set, for a domain chosen by the instructor, to use in the rest of the project. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early.
- Late work and simple extensionnot accepted for these assessments.
- When special consideration is approved, the appropriate consideration should be “extension of time”.
Project Stage 2: The group will use computational tools to explore the data, and report on both what was done and what was found (using appropriate summaries and charts). It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early.
- Late work and simple extensionnot accepted for these assessments.
- When special consideration is approved, the appropriate consideration should be “extension of time”.
Project Stage 3: The group will use computational tools to produce a predictive model for some aspect of the data, and evaluate this model; deliverable is a report on both what was done and what was found. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early.
- Late work and simple extensionnot accepted for these assessments.
- When special consideration is approved, the appropriate consideration should be “extension of time”.
Project Stage 4: The group will use computational tools to produce an interactive visualisation for some aspect of the data; deliverable is a report on what was done and a system that provides the interaction. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early.
- Late work and simple extensionnot accepted for these assessments.
- When special consideration is approved, the appropriate consideration should be “extension of time”.
Exam: The exam contains multiple-choice and essay questions. Covering knowledge, conceptual content, skills, and experiences.
- Late work and simple extensionare not accepted for these assessments.
- When special consideration is approved, the appropriate action should be “replacement exam”.
Detailed information for each assessment can be found on Canvas.
Minimum requirement: It is a double-pass policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

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:

Late work and simple extensions are not accepted for the following assessments: Weekly coding tasks, Weekly revision quizzes, Python coding test, final exam, and for all the unweighted formative assessments (practice assessments). Late work is accepted up till 10 days late, subject to the standard penalties (subtract 5% of maximum possible mark, per day late) for Project stage 1, Project stage 2, Project stage 3, Project stage 4. However, note that work submitted late may not receive feedback before the next stage is due.

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 and how to learn to program. Data science life-cycle and key data concepts.	Lecture (1 hr)
	Use Python as calculator. Examine a dataset. Unix tools.	Tutorial (2 hr)
	Advanced topic: Python file system basics (os, pathlib)	Lecture (1 hr)
Week 02	Python concepts.	Lecture (1 hr)
	Calculate with built-in functions; text processing and data cleaning.	Tutorial (2 hr)
	Advanced topic: Basic algorithmic thinking (sorting, counting, loop patterns)	Lecture (1 hr)
Week 03	Python conditionals and loops. Strings and textfiles.	Lecture (1 hr)
	Summarise a dataset; Advanced text processing 1. Advanced: Machine Learning (Vectors and Vector Operations, Matrices and Matrix Operations)	Tutorial (2 hr)
	Advanced topic: Review of Linear Algebra Basics for Machine Learning 1 (Vectors and Vector Operations, Matrices and Matrix Operations)	Lecture (1 hr)
Week 04	Python lists. Data aggregation patterns.	Lecture (1 hr)
	Advanced text processing 2; Bucketing and pivoting numeric data 1. Group formation. Advanced: Review of Statistics Basics for Machine Learning (Random Variables, Probability, Distribution)	Tutorial (2 hr)
	Advanced topic: Review of Statistics Basics for Machine Learning 1 (Random Variables, Probability, Distribution)	Lecture (1 hr)
Week 05	Python dictionaries. Data quality.	Lecture (1 hr)
	Bucketing and pivoting numeric data 2. Stage 1 work.	Tutorial (2 hr)
	Advanced topic: (Code demos) Vectors and Vector Operations, Matrices and Matrix Operations, Sample from Distribution in python	Lecture (1 hr)
Week 06	Using modules (csv and pandas). Functions	Lecture (1 hr)
	Using modules and functions. Stage 1 work. Advanced: Review of Linear Algebra Basics for Machine Learning (Linear Transformations, System of Equations)	Tutorial (2 hr)
	Advanced topic: Review of Linear Algebra Basics for Machine Learning 2 (Linear Transformations, System of Equations)	Lecture (1 hr)
Week 07	Data format. Communication principles.	Lecture (1 hr)
	More about Pandas. Stage 2 work. Advanced: Review of Statistics Basics for Machine Learning 2 (Statistical Dependency)	Tutorial (2 hr)
	Advanced topic: Review of Statistics Basics for Machine Learning 2 (Statistical Dependency)	Lecture (1 hr)
Week 08	Chart concepts.	Lecture (1 hr)
	Chart evaluation and design. Using Matplotlib. Stage 2 work.	Tutorial (2 hr)
	Advanced topic: (Code demos) Solving Linear Systems and Exploring Linear Transformations, Statistical Dependency and Conditional Probability with Python	Lecture (1 hr)
Week 09	Introduction to machine learning. Clustering and recommenders.	Lecture (1 hr)
	Using scikit-learn modules. Stage 2 work. Advanced: similarity measurement	Tutorial (2 hr)
	Advanced topic: Inspecting some clustering algorithms	Lecture (1 hr)
Week 10	Predictivie models.	Lecture (1 hr)
	In-class Python coding test. Stage 2 work.	Tutorial (2 hr)
	Advanced topic: k-NN and decision tree algorithms	Lecture (1 hr)
Week 11	More Machine Learning concepts. Natural language processing (NLP).	Lecture (1 hr)
	Catch-up on Scikit. Python exceptions. Stage 2 work. Advanced: NLP tasks - labelling/annotation	Tutorial (2 hr)
	Advanced topic: NLP tasks - labelling/annotation	Lecture (1 hr)
Week 12	Data management. Number formats.	Lecture (1 hr)
	In-class group project Stage 2 presentation.	Tutorial (2 hr)
	Advanced topic: NLP tasks - inter-raters' agreement	Lecture (1 hr)
Week 13	Ethics and fairness in data science; Semester review and exam preview (shared data1002 material).	Lecture (1 hr)
	Peer-assess practice exam answers. Advanced: Inter-raters agreement	Tutorial (2 hr)
	Advanced topic: revision of advanced topics, preview of advanced exam questions	Lecture (1 hr)
Weekly	Read slides and/or watch prerecorded videos, before lecture timeslot; contribute on discussion boards; do online quiz then correct mistakes; do online Python and shell tasks; either: pre-work to prepare for lab, or else work on project (approx 5 hrs/wk)	Independent study (65 hr)

Attendance and class requirements

Laboratories: attendance at lab sessions is crucial, as this is where groups are formed, work together, and get feedback on the project stages.

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

These books are optional extra reading; they can be accessed through the Library eReserve, available on Canvas.

J. Grus, Data Science from Scratch 2nd ed. O`Reilly, 2019. isbn 1492041130.
J. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data 2nd ed. MIT Press, 2016. isbn 0262529629.

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. automate a computational process, when given a clear account of the algorithm to be applied (to be done by writing Python programs with core techniques of procedural programming)
LO2. demonstrate knowledge of Python syntax and semantics, to trace and understand idiomatic code typical of data science activities, including features such as user-defined functions, exception-raising, and handling
LO3. understand automation of the computational process needed for examples of the various activity in the data science pipeline: data ingestion and cleaning, data format conversion, data summarization, visual and tabular presentation of the results from summarization, creation of a predictive model of a given form, application of a predictive model to new data, evaluation of a predictive model (and also, automation of a pipeline that scripts use of existing tools for these activities)
LO4. understand programs in Python to automatically perform computational processes of data science, and awareness of the similarities and differences between tools
LO5. understand main issues for data management in connection with data science activities, including value of data, importance of metadata, and issues when sharing data across time and users
LO6. understand how data sets are represented in computer files, in particular, the many-to-many relationship between the physical representation and the logical representation; advantages and disadvantages of different representations
LO7. understand principles of charting and information presentation, and ability to produce good charts using Python libraries; also capability to evaluate charts for effectiveness in communication.
LO8. understand principles of machine learning and its role in data science, in particular creation, use, and limitations of predictive models for regression and classification tasks, issues of over-fitting and under-fitting, and evaluation of models.
LO9. understand the basic machine learning related mathematics, such as linear algebra and statistics

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.

Unit has been adjusted following feedback from last year survey. The workload is reduced so that students can better cope with the unit. Since it is a flip learning unit, the "lecture" will be made more on the problem-based approach. The unix concepts are replaced by machine learning related mathematics.

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

Early feedback task

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

DATA1902: Informatics: Data and Computation (Advanced)

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

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