Unit outline_

DATA1902: Informatics: Data and Computation (Advanced)

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

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 Josiah Poon, josiah.poon@sydney.edu.au
The census date for this unit availability is 31 August 2026
Type Description Weight Due Length Use of AI
Written exam hurdle task Exam
CLOSED book exam with MC and short essay questions.
50% Formal exam period 2 hours AI prohibited
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
Practical skill Weekly coding tasks
Experience and practice Python coding.
5% Multiple weeks n/a AI allowed
Outcomes assessed: LO1 LO2
Out-of-class quiz Early Feedback Task Early Feedback Task - MCQ
MCQ on w1-3 materials
2% Week 03
Due date: 23 Aug 2026 at 17:00
n/a AI allowed
Outcomes assessed: LO1 LO2 LO3 LO9
In-person practical, skills, or performance task or test PRACTICE Python coding test
Prepare students for the in-class Python automated tests
0% Week 09 - AI prohibited
Outcomes assessed: LO1 LO2
Data analysis group assignment Project stage 1 - Report
Report: describe dataset, metadata, how data was cleaned and ingested, data summaries and charts, describe computation, evaluation
20% Week 09
Due date: 11 Oct 2026 at 17:00
n/a AI allowed
Outcomes assessed: LO1 LO3 LO4 LO5 LO9
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 10 60 minutes AI prohibited
Outcomes assessed: LO8 LO1 LO2
Data analysis group assignment Project stage 2 - Poster Presentation
in-class POSTER presentation on predictive model and evaluation of its success
8% Week 12 n/a AI allowed
Outcomes assessed: LO8 LO10 LO1 LO3 LO7
Data analysis Text analysis
Text Analytics and Machine Learning Investigation
5% Week 13
Due date: 08 Nov 2026 at 17:00
- AI allowed
Outcomes assessed: LO1 LO2 LO8 LO9
hurdle task = hurdle task ?
group assignment = group assignment ?
early feedback task = 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 understand their progress and level of understanding, so that they can make informed decisions about their study plan for this unit.

    • 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 tasks can start in any time but are recommended to start in tutorials (the latest) and completed in the student's own time
    • 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.
  • Practice Python coding test: Done in student’s own time. There are several tasks in the test. The aim is to calculate a precisely described output that extends 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 extension are not accepted for these assessments. 
    • When the special consideration is approved for the first time, the student will take a supplementary test at 10-11am on 17 October 2026 (Saturday).
    • If a student cannot attend the supplementary test and Special Consideration is approved, the outcome will be mark adjustment, where the assessment mark will be calculated in proportion to the student's final examination 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. The group will also use computational tools to explore the data, and report on both what was done and what was found (using appropriate summaries and charts). A report is required. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early.
    • Stage 2:
      • The group will use computational tools to produce a predictive model for some aspect of the data, and evaluate this model; deliverable is a A0 or A1 poster 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.
    • Special Consideration for both Stage 1 & 2:
      • Late submissions and simple extensions are not available for these assessments.
      • The Stage mark comprises a group component and an individual component. The group mark is conditional upon submission of the student's individual component. Students who do not submit their individual component will not receive the group mark.
      • If a student's first application for Special Consideration is approved, the outcome will be an extension of up to two weeks. The Stage mark will be released only after the individual component has been submitted.
      • If a second application for Special Consideration for the same assessment is approved, no further extension will be granted. Instead, the assessment mark will be determined by mark adjustment, using the same proportion as the student's final examination mark, in accordance with the approved Special Consideration outcome.
  • Exam: The exam contains multiple choice and essay questions, covering knowledge, conceptual understanding, practical skills, and learning outcomes.
    • Late work and simple extesnsion are 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, to pass this unit, students must achieve at least 40% in the written examination. 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 submissions and simple extensions are not accepted for the following assessments: • Weekly coding tasks • Python coding test • Final examination • All unweighted formative assessments (practice assessments). Late submissions are accepted for Project Stage 1 and Project Stage 2 for up to 10 calendar days after the due date. A late penalty of 5% of the maximum available mark per calendar day will apply. Please note that submissions received after the deadline may not receive feedback before the next project 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.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction and how to learn to program. Data science life-cycle and key data concepts. What Is GenAI and Why Is It Different? Lecture (1 hr) LO3 LO4 LO5 LO9
Use Python as calculator. Examine a dataset. Tutorial (2 hr) LO1 LO2 LO3 LO5
(Adv topic) Why Data Science Projects Are Different from Software Projects Lecture (1 hr) LO4 LO10
Week 02 Python concepts. Why GenAI Makes Mistakes? Lecture (1 hr) LO1 LO2 LO4 LO5 LO9
Calculate with built-in functions; text processing and data cleaning. (Adv topic) Vectors, Matrices, and Computational Thinking Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO9 LO10
(Adv topic) The Language of Data: Vectors, Matrices, and Computational Thinking Lecture (1 hr) LO4 LO10
Week 03 Python conditionals and loops. Strings and textfiles. Why GenAI Makes Mistakes? Lecture (1 hr) LO1 LO2 LO6 LO9
Summarise a dataset; Advanced text processing 1. (Adv topic) Reasoning Under Uncertainty Tutorial (2 hr) LO1 LO2 LO3 LO4 LO9 LO10
(Adv topic) Reasoning Under Uncertainty: Why Data Never Tells the Whole Story Lecture (1 hr) LO10
Week 04 Python lists. Data aggregation patterns. Search, Context and Memory. Lecture (1 hr) LO1 LO2 LO3 LO6 LO9
Advanced text processing 2; Bucketing and pivoting numeric data 1. Group formation. (Adv topic) Learning from Evidence Tutorial (2 hr) LO1 LO2 LO3 LO9 LO10
(Adv topic) Learning from Evidence: Updating Beliefs with Data Lecture (1 hr) LO10
Week 05 Python dictionaries. Data quality. Human AI Collaboration with 3C Lecture (1 hr) LO1 LO2 LO3 LO6 LO9
Bucketing and pivoting numeric data 2. Stage 1 work. (Adv topic) Finding Relationships in Data Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6 LO9 LO10
(Adv topic) Finding Relationships in Data: Correlation, Prediction, and Interpretation Lecture (1 hr) LO1 LO3 LO10
Week 06 Using modules (csv and pandas). Functions. STEWARD: A Framework for Human AI Synergy. Lecture (1 hr) LO5 LO6 LO7 LO9
Using modules and functions. Stage 1 work. (Adv topic) Optimisation Through Gradient Descent Tutorial (2 hr) LO1 LO5 LO6 LO9 LO10
(Adv topic) How Machines Learn: Optimisation Through Gradient Descent Lecture (1 hr) LO10
Week 07 Data format. Communication principles. Applying GenAI Across the Data Science Lifecycle. Lecture (1 hr) LO1 LO2 LO3 LO4 LO6 LO7 LO9
More about Pandas. Stage 2 work. (Adv topic) Algorithm Efficiency and Scalability Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO7 LO9 LO10
(Adv topic) When Data Gets Big: Algorithm Efficiency and Scalability Lecture (1 hr) LO10
Week 08 Chart concepts. Trustworthy AI and Decision Making Lecture (1 hr) LO1 LO2 LO3 LO6 LO7 LO9
Chart evaluation and design. Using Matplotlib. Stage 2 work. (Adv topic) Search Algorithms and Intelligent Problem Solving Tutorial (2 hr) LO2 LO3 LO7 LO9 LO10
(Adv topic) Search Algorithms and Intelligent Problem Solving Lecture (1 hr) LO1 LO3 LO7 LO10
Week 09 (Public holiday- Video recording) Introduction to machine learning. Clustering and recommenders. Lecture (1 hr) LO1 LO2 LO3 LO4 LO8
Using scikit-learn modules. Stage 2 work. (Adv topic) Grouping Similar Things Tutorial (2 hr) LO1 LO2 LO3 LO8 LO10
(Adv topic) Inspecting some clustering algorithms Lecture (1 hr) LO3 LO7 LO10
Week 10 Predictivie models. Lecture (1 hr) LO1 LO2 LO3
In-class Python coding test. Stage 2 work. (Adv topic) Decision Trees Tutorial (2 hr) LO1 LO2 LO3 LO8 LO10
(Adv topic) k-NN and decision tree algorithms Lecture (1 hr) LO3 LO7 LO10
Week 11 More Machine Learning concepts. Natural language processing (NLP). Lecture (1 hr) LO2 LO3 LO8
Catch-up on Scikit. Python exceptions. Stage 2 work. (Adv topic) Data Labelling and Annotation Tutorial (2 hr) LO1 LO2 LO3 LO8 LO10
(Adv topic) NLP tasks - labelling/annotation Lecture (1 hr) LO7 LO10
Week 12 Data management. Number formats. Lecture (1 hr) LO1 LO2 LO3 LO5
In-class group project Stage 2 presentation. (Adv topic) Measuring Annotation Quality and Reliability Tutorial (2 hr) LO1 LO2 LO3 LO5 LO8 LO10
(Adv topic) NLP tasks - inter-raters' agreement Lecture (1 hr) LO10
Week 13 Ethics and fairness in data science; Poster competition; Semester review and exam preview.(shared data1002 material). Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Peer-assess practice exam answers. (Adv topic) Inter-raters agreement Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO10
(Adv topic) revision of advanced topics, preview of advanced exam questions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO9 LO8 LO10
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) Self-directed learning (65 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO9 LO8

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.
  • An AF grade will be automatically awarded to a student if the student attends less than 6 weeks in the period from week 4 to week 13 (inclusive), without Special Consideration approval.

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 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.

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 principles and cautions on the use of GenAI in the Data Science lifecycle
  • LO10. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

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