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	Josiah Poon, josiah.poon@sydney.edu.au
Lecturer(s)	Josiah Poon, josiah.poon@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Exam A mix of multiple-choice, short answer, longer (eg half-page) discussions.	50%	Formal exam period	2 hours
Outcomes assessed:
Online task	Weekly quizzes Answer multichoice questions as Canvas quiz (done before lecture)	10%	Multiple weeks	N/A
Outcomes assessed:
Skills-based evaluation	Weekly coding tasks Produce Python and shell code to meet automated tests (participation mark)	5%	Multiple weeks	N/A
Outcomes assessed:
Assignment	Project stage 1 Report describes dataset, metadata, and how data was cleaned and ingested.	5%	Week 06	n/a
Outcomes assessed:
Skills-based evaluation	Practice Python coding test A Extend Python code to pass automated tests	0%	Week 07	60 minutes
Outcomes assessed:
Skills-based evaluation	Practice Python coding test B Produce Python code that passes automated tests	0%	Week 07	60 minutes
Outcomes assessed:
Assignment	Project stage 2 Report: data summaries and charts, describe computation, evaluation.	10%	Week 09	n/a
Outcomes assessed:
Skills-based evaluation	Python coding test A In a scheduled timeslot, extend Python code to pass automated tests	5%	Week 10	60 minutes
Outcomes assessed:
Skills-based evaluation	Python coding test B In a scheduled timeslot, produce Python code that passes automated tests	5%	Week 10	60 minutes
Outcomes assessed:
Assignment	Project stage 3 Report with predictive model and evaluation of its success.	5%	Week 12	n/a
Outcomes assessed:
Assignment	Project stage 4 (advanced) Produce interactive visualisation of data	5%	Week 13	n/a
Outcomes assessed:
= group assignment ? = Type B final exam ?

Assessment summary

Weekly coding tasks: Tasks where the student must write a Python or shell 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 is 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 quizzes: Each quiz consists of multiple-choice questions related to the lecture, lab, or tutorial content from the previous week. Done in students own time. Late work is 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 A: Done in student’s own time. There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates 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”.
Practice Python coding test B: Done in student’s own time. The student must produce Python code that calculates precisely described output from data in a file. This assessment 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 A: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates 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 is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
Python coding test B: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). The student must produce Python code that calculates precisely described output from data in a file. This assessment is graded automatically, by comparing the output produced on several different inputs to what is described. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
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. 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. 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. 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. If special consideration is approved, the appropriate consideration should be “extension of time”.
Exam: An online exam, with multiple-choice, short-answer, longer (eg half-page) discussions. Covering knowlede, conceptual content, skills, and experiences. Some of the questions are common with data1002 final exam, and some are distinct on the additional topics covered. Late work is not accepted for this assessment. 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 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.

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.

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 is not accepted for the following assessments: Weekly coding tasks, Weekly quizzes, Python coding test A, Python coding test B, 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 Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and how to learn to program. Data science life-cycle and key data concepts	Lecture (2 hr)
	Online Tutorial: Use Python as calculator. Online Lab: Examine a dataset	Tutorial (2 hr)
	Advanced topic: Unix environment	Lecture (1 hr)
Week 02	Python concepts. Spreadsheet concepts	Lecture (2 hr)
	Online tutorial: calculate with built-in functions; text processing and data cleaning. Online practical: Spreadsheet data	Tutorial (2 hr)
	Advanced topic: shell scripts on text files	Lecture (1 hr)
Week 03	Python conditionals and loops. Strings and textfiles	Lecture (2 hr)
	Online tutorial: Summarise a dataset; Advanced text processing 1. Online lab: Spreadsheet calculations	Tutorial (2 hr)
	Advanced topic: Shell variables and working with numbers	Lecture (1 hr)
Week 04	Python lists. Data aggregation patterns	Lecture (2 hr)
	Online tutorial: Advanced text processing 2; Bucketing and pivoting numeric data 1. Online lab: group formation	Tutorial (2 hr)
	Advanced topic: Regular expressions	Lecture (1 hr)
Week 05	Python dictionaries. Data quality.	Lecture (2 hr)
	Online tutorial: Bucketing and pivoting numeric data 2. Online lab: Stage 1 work.	Tutorial (2 hr)
	Advanced topic: AWK scripts	Lecture (1 hr)
Week 06	Data format. Stage 1 discussion; communication principles.	Lecture (2 hr)
	Online tutorial: catchup Python skills; data formats and representation. Online lab: Stage 1 work.	Tutorial (2 hr)
	Advanced topic: More AWK	Lecture (1 hr)
Week 07	Using modules (csv and pandas). Chart concepts 1.	Lecture (2 hr)
	Online tutorial: use Python matplotlib module for charts. Online lab: Stage 2 work.	Tutorial (2 hr)
	Advanced topic: comparing shell scripts with other tools for data science processing	Lecture (1 hr)
Week 08	Chart concepts 2. Number formats.	Lecture (2 hr)
	Online tutorial: chart evaluation and design. Online lab: Stage 2 work.	Tutorial (2 hr)
	Advanced topic: charting with Python's bokeh module	Lecture (1 hr)
Week 09	introduction to machine learning. Stage 2 discussion; Regression and classification predictive models.	Lecture (2 hr)
	Online tutorial: revise Python coding skills; use Python csv and pandas modules. Online lab: Stage 2 work.	Tutorial (2 hr)
	Advanced topic: customised interaction with Python's bokeh module	Lecture (1 hr)
Week 10	Scheduled timeslots for Python coding test A and Python coding test B.	Lecture (2 hr)
	Online tutorial: use Python scikit-learn module. Online lab: Stage 3 work.	Tutorial (2 hr)
	Advanced topic: linking charts with Python's bokeh module	Lecture (1 hr)
Week 11	Persistent data. Python functions and scope.	Lecture (2 hr)
	Online tutorial: Data security; version control. Online lab: Stage 3 work.	Tutorial (2 hr)
	Advanced topic: evaluating interactive visualisations	Lecture (1 hr)
Week 12	Stage 3 discussion; Python exceptions. More Machine Learning approaches (clustering and recommenders).	Lecture (2 hr)
	Online tutorial: Python exceptions. Online lab: stage 3 work.	Tutorial (2 hr)
	Advanced topic: Natural Language Processing (NLP)	Lecture (1 hr)
Week 13	Ethics and fairness in data science; Semester review and exam preview (shared data1002 material).	Lecture (2 hr)
	Online tutorial: peer-assess practice exam answers. Online lab: peer-assess practice exam answers.	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 both spreadsheets, and programs in Python, for automatically performing 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 both Python libraries and spreadsheets; 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. use and understand some more sophisticated tools for computation or data-handling.

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 the initial move online in 2020, to support increased delivery of feedback on assessments and more effective division of work for group assessments.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect

Current students

DATA1902: Informatics: Data and Computation (Advanced)

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Media

Student links

About us

Connect