Outline

Overview

This course focuses on methods and techniques to efficiently explore and analyse large data collections. Where are hot spots of pedestrian accidents across a city? What are the most popular travel locations according to user postings on a travel website? The ability to combine and analyse data from various sources and from databases is essential for informed decision making in both research and industry. Students will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects, such as relational, semi-structured, time series, geospatial, image, text. As well as reinforcing their programming skills through experience with relevant Python libraries, this course will also introduce students to the concept of declarative data processing with SQL, and to analyse data in relational databases. Students will be given data sets from, eg. , social media, transport, health and social sciences, and be taught basic explorative data analysis and mining techniques in the context of small use cases. The course will further give students an understanding of the challenges involved with analysing large data volumes, such as the idea to partition and distribute data and computation among multiple computers for processing of 'Big Data'.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	DATA1002 or DATA1902 or INFO1110 or INFO1910 or INFO1903 or INFO1103 or ENGG1810
Corequisites ?	None
Prohibitions ?	DATA2901
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mohammad Polash, masbaul.polash@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Examination Final exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Weekly Homework weekly online quiz in Canvas	5%	Multiple weeks	20 min each week	AI allowed
Outcomes assessed:
In-class quiz	Early feedback task Early feedback task: This would be an in-class assessment during tutorial time of week 3. This will cover the contents from Lectures 1-2.	2%	Week 03	15 minutes	AI allowed
Outcomes assessed:
In-person written or creative task	SQL Test SQL invigilated restricted online test.	8%	Week 06	1 hour	AI prohibited
Outcomes assessed:
In-person written or creative task	MCQ and short answers test Supervised and restricted small test during Week 9 lecture - Pen and paper-based	15%	Week 09	50 minutes	AI prohibited
Outcomes assessed:
Data analysis	Practical Assignment Practical data integration and data analysis assignment.	20%	Week 11 Due date: 18 May 2025 at 23:59	4 weeks	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

Homework: Short weekly homework quizzes in Canvas. They are designed to help you review your learning of each week’s topic.
Reflection Notes: Brief reflection of your learning on each week. They are designed to help retention.
SQL tutorials and SQL Test: Students work through weekly online tutorials introducing increasingly sophisticated usage of SQL. The SQL tutorials provide simple feedback and allow multiple attempts, and example solutions are available after the submission deadline has passed. Solutions are provided for each week, and the topics are assessed in a mid-semester SQL test.
Practical Assignment: Students work in teams on a larger data integration and data analysis task, where some supplied datasets have to be combined with additional data researched by students. The final submission consists of the source code artifacts developed by the teams, plus a short report of their findings, and a group demo during the labs of Week 12.
Final Examination: Understanding of all of this unit’s material is reviewed.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, representing complete or close to complete mastery of the material.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at an excellent standard, representing excellence, but substantial less than complete mastery.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, representing creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, representing at least routine knowledge over a spectrum of topics and important ideas and concepts taught in this unit of study.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

Minimum Pass 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.

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.

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
Multiple weeks	SQL self-study tutorials on Ed	Self-directed learning (16 hr)
Multiple weeks	Project Work (practical assignment) - own time	Self-directed learning (16 hr)
Ongoing	revision of weekly material and working on weekly lab exercises (homework)	Self-directed learning (40 hr)
Week 01	Introduction and Motivation: What is big data? Challenges for data analytics	Lecture (2 hr)
Week 02	Accessing data in relational databases; Introduction to SQL	Lecture (2 hr)
Week 02	Accessing data in relational databases	Tutorial (2 hr)
Week 03	Declarative data analysis with SQL	Lecture (2 hr)
Week 03	Declarative data analysis with SQL	Tutorial (2 hr)
Week 04	Data Cleaning and Exploration with Python; Intro Jupyter Notebooks	Lecture (2 hr)
Week 04	Introduction to Jupyter Notebooks and Data Cleaning and Exploration with Python	Tutorial (2 hr)
Week 05	Scalable data analytics: The role of indexes and data partitioning	Lecture (2 hr)
Week 05	Scalable data analytics	Tutorial (2 hr)
Week 06	Web Scraping: reading and interpreting data from the web	Lecture (2 hr)
Week 06	Web Scraping	Tutorial (2 hr)
Week 07	Web APIs and NoSQL: Working with semi-structured data	Lecture (2 hr)
Week 07	Web APIs and NoSQL	Tutorial (2 hr)
Week 08	Geo-Spatial Data	Lecture (2 hr)
Week 08	Geo-Spatial Data	Tutorial (2 hr)
Week 09	Text data processing: feature extraction and analysis	Lecture (2 hr)
Week 09	Text data processing	Tutorial (2 hr)
Week 10	Timeseries Data / Health Data	Lecture (2 hr)
Week 10	Timeseries Data	Tutorial (2 hr)
Week 11	Image data processing: feature extraction and analysis	Lecture (2 hr)
Week 11	Image data processing	Tutorial (2 hr)
Week 12	Challenges in analysing Big Data; Data privacy/anonymising data	Lecture (2 hr)
Week 12	Aspects of Big Data	Tutorial (2 hr)
Week 13	Revision	Lecture (2 hr)
Week 13	Revision and Exam Preparation	Tutorial (2 hr)

Attendance and class requirements

Students are expected to follow the weekly lectures, either in class or via the lecture recordings, and to work through the lab/practical material. During the first six weeks of the unit, an online SQL tutorial is also available, which students are expected to work through on their own time to prepare for the mid-semester SQL quiz.

The practical assignment is group work, where all team members are expected to actively participate and divide the work fairly. The individual mark awarded for the group assignment is conditional on a team member being able to explain any part of the group submission to the tutor or the lecturer if asked. In particular, groups will have to demo their submissions in the labs of Week 12, and based on this demo, the group’s assignment mark will be scaled for each team member based on each team member's level of contribution. Further details on this participation scaling will be provided in the assignment handout.

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.

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. use appropriate Python libraries to automate data science activities on diverse kinds of data
LO2. Ingest, combine and summarise data from a variety of data models
LO3. demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text, including experience to combine data of different types
LO4. understand and produce declarative queries to extract appropriate information from data sets, including competence in use of SQL
LO5. understand the main challenges analysing 'big data': data volume, variety, velocity, veracity
LO6. understand the impact of data volume on data processing, and have awareness of approaches to address this such as indexing, compression, data partitioning, and distributed processing frameworks (Hadoop).
LO7. demonstrate awareness of privacy issues when working with data

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.

Based on student feedback, the assessments have been rearranged throughout the semester.

Additional information

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Computer programming assignments may also be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/) . These programs work in a similar way to TII in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

DATA2001: Data Science, Big Data and Data Variety

Semester 1, 2026 [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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links