Outline

Overview

The ubiquitous use of information technology leaves us facing a tsunami of data produced by users, IT systems and mobile devices. The proper management of data is hence essential for all applications and for effective decision making within organizations. This unit of study will introduce the basic concepts of database designs at the conceptual, logical and physical levels. We will place particular emphasis on introducing integrity constraints and the concept of data normalization which prevents data from being corrupted or duplicated in different parts of the database. This in turn helps in the data remaining consistent during its lifetime. Once a database design is in place, the emphasis shifts towards querying the data in order to extract useful information. The unit will introduce the SQL database query languages, which is industry standard. Other topics covered will include the important concept of transaction management, application development with a backend database, and an overview of data warehousing and OLAP.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	INFO1110 or INFO1910 or ENGG1810 or DATA1002
Corequisites ?	None
Prohibitions ?	INFO2120 or INFO2820 or COMP5138
Assumed knowledge ?	6 credit points of MATH or STAT units or DATA1001
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mohammad Polash, masbaul.polash@sydney.edu.au
Lecturer(s)	Alan Fekete, alan.fekete@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Final Exam 2 hr duration, in-person on campus, hand-writing on paper	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	weekly quizzes Secure quiz done online, in tutorial class	21%	Multiple weeks	20 minutes each	AI prohibited
Outcomes assessed:
Practical skill	SQL tasks Online tasks to write SQL queries that have given output	6%	Multiple weeks	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Early feedback quiz Multichoice quiz, in tutorial class	3%	Week 03	20 minutes	AI prohibited
Outcomes assessed:
Creative work	Conceptual Model Assignment Production of conceptual model for a domain	10%	Week 07 Due date: 20 Sep 2026 at 23:59 Closing date: 27 Sep 2026	n/a	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	SQL Online Quiz Produce SQL queries to meet particular information needs	10%	Week 09	50 minutes	AI prohibited
Outcomes assessed:
Practical skill	Exam practice assignment Answer questions on unit concepts, especially those using theory	0%	Week 12 Due date: 01 Nov 2026 at 23:59 Closing date: 01 Nov 2026	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

Open:

SQL Tasks (multiple weeks) 6% [each task worth 0.6 points; total capped at 6]

Conceptual Model assignment (week 7) 10% - group work

Exam practice assignment (week 12) 0% - sample solution will be provided one week after due date; feedback on submissions is for formative purposes only

Secure:

Early feedback task (secure; in scheduled tutorial in week 3) 3%

Weekly quizzes (secure; multiple weeks in scheduled tutorial) 21% [each quiz worth 3 points; total capped at 21]

SQL Online Quiz (secure; in scheduled tutorial in week 9) 10%

Final exam (secure, inperson, on paper) 50%

Assessment criteria

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.

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.

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 is not accepted for secure assessments (early feedback quiz, SQL Online Quiz, final exam, weekly quizzes); special consideration for these results in replacement secure assessments. Late work is not accepted for SQL tasks; where special consideration is granted for these assessments, reweighting of other relevant tasks will be applied. Late work is not accepted for the zero-weight exam practice assignment; no special consideration can be made. Late submissions for conceptual design group assignments will incur a penalty of 5% of the maximum awardable marks for each day, or part-day, past the due date, up to the closing date (as after this time, feedback on on-time submissions will be available, resulting in an unfair advantage if submissions after this time were accepted). After closing date, late submissions will not be accepted. Where special consideration is granted for these assessments, extensions no later than closing date will be permitted. After closing date, reweighting of other relevant tasks will be applied.

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, Administrativa, Overview of the relational approach to data, role of data and dbms in organisations, and relevant job roles	Lecture (2 hr)
Week 02	Core SQL constructs (SELECT-FROM-WHERE, aggregates, joins); Entity-Relationship notation (and extensions) for expression of a conceptual data model	Lecture (2 hr)
Week 02	Getting to know one another; Entity-relationship diagrams; Quiz	Tutorial (2 hr)
Week 03	Converting an ER conceptual design to a relational schema; SQL schema definition commands (including simple integrity constraints); SQL modification syntax (INSERT, DELETE, UPDATE)	Lecture (2 hr)
Week 03	Convert ER diagram to relational schema; administration and queries on PostgreSQL server; Early feedback quiz	Tutorial (2 hr)
Week 04	Relational algebra and its relationship with SQL; More Complex SQL (including subqueries, handling of nulls, outer joins)	Lecture (2 hr)
Week 04	Relational algebra; SQL grouping and aggregation; quiz; group formation	Tutorial (2 hr)
Week 05	How to produce a conceptual data model for a domain; review of conversion from conceptual model to relational schema	Lecture (2 hr)
Week 05	Understand conceptual data model; process of conceptual design; quiz	Tutorial (2 hr)
Week 06	Evaluating and improving relational schema; Relational design theory (functional dependencies, Boyce-Codd Normal Form; schema decomposition)	Lecture (2 hr)
Week 06	Relational design and normalisation; quiz	Tutorial (2 hr)
Week 07	Data security and privacy - goals, attacks, protection mechansims (views; access control; triggers and sophisticated integrity mechanisms; stored procedures, anonymization, data perturbation)	Lecture (2 hr)
Week 07	Access control; integrity; privacy; quiz	Tutorial (2 hr)
Week 08	DB Applications (architecture, technology choices, development approaches); security for DB applications	Lecture (2 hr)
Week 08	Data-backed application code; quiz	Tutorial (2 hr)
Week 09	General feedback on Conceptual Model assignment; overview of dbms implementation concepts (architectural choices, query processing)	Lecture (2 hr)
Week 09	SQL Online quiz; data-backed applications and their secuirty	Tutorial (2 hr)
Week 10	More on Dbms implementation concepts (physical storage including indexes, buffers; implications in query processing); performance tuning	Lecture (2 hr)
Week 10	DBMS implementation (indexing and performance); quiz	Tutorial (2 hr)
Week 11	More on dbms implementation concepts, including transactions and security and privacy support	Lecture (2 hr)
Week 11	Database implementation (transactions, privacy and security support); quiz	Tutorial (2 hr)
Week 12	Overview of enterprise-scale data management and governance; data integration	Lecture (2 hr)
Week 12	Data integration and analytics, governance; quiz	Tutorial (2 hr)
Week 13	General feedback on Exam practice assignment; Revision, exam preview	Lecture (2 hr)
Week 13	Exam preparation; quiz	Tutorial (2 hr)

Attendance and class requirements

Study commitment: A variety of learning situations will be employed during the unit of study, including lectures, on-line demos, tutorials or directed computer laboratory exercises, self-learning SQL exercises (`SQL Lessons`), quizzes, assessed group assignment, non-assessed assignment for formative feedback. To benefit fully from this unit it is necessary to engage fully in all aspects of the unit of study week-by-week.
Tutorials: Tutorial work includes hands-on use of DBMS and practice in problem-solving related to the content. Attendance at tutorial is crucial for learning, and assessments.
Independent Study and Group work: Work on assignments, practice on SQL Lessons, reading lecture notes or other material, etc; this should allow students to engage with the material and to integrate it into their understanding. At least 5 hours independent study is expected each week.

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. understand the concept of a DBMS, differences from other ways to store and share data, DBMS role in organisations, and the types of work done with a DBMS
LO2. understand the relational data model: connect relational data to real world facts, and vice versa; know limitations and benefits of the relational model approach
LO3. work with data stored in a relational database management system: understand table definitions including integrity constraints, extract information through SQL queries, modify information through SQL queries
LO4. design a suitable schema which says how information about a particular domain will be stored in a relational DBMS: create a conceptual data model for a domain, produce relational schema (including integrity constraints) from a conceptual model, apply normalisation theory to evaluate or improve a relational schema
LO5. understand how application software can use data stored in a relational DBMS, and understand the basic architectural alternatives for data management applications
LO6. understand goals, threats, and protection techniques, for ensuring data security and privacy, including use of SQL views, access control, integrity constraints, stored procedures
LO7. understand some concepts of dbms implementation that impact on application quality and performance, including query processing, index structures, transactions
LO8. connect general database concepts to both theoretical abstract formulations, and details of specific software platforms.
LO9. work effectively in a team with members whose skills and interests differ

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.

Response to impact of generativeAI: Increase focus on evaluation and problem clarification; reduction in number of marked assignments; increase in weight on secure assessments; tutorials now 2 hours each week (from week 2).

Additional information

Recommended reading is from one at least of the following 4 textbooks:

“Database Systems Concepts” (7th edition, 2019), by A. Silberschatz, H. Korth, S. Sudarshan; Published by McGraw-Hill; isbn: 0078022150

“Database Management Systems” (3rd edition, 2002), by R. Ramakrishnan , J. Gerhke; Published by McGraw-Hill, isbn: 0072465638; in library at 005.74 177A

“Database Systems: The Complete Book” (2nd edition, 2008), by H. Garcia-Molina, J. Ullman, J. Widom; Published by Pearson; isbn: 0131873253; in library: at 005.74 233A

“Database Systems: An Application-Oriented Approach, Complete Version” (2nd edition, 2005) by M. Kifer, A. Bernstein, P. Lewis; Published by Pearson, isbn: 0321268458; in library: at 005.74 221B

Another useful reference is:

SQL Cookbook: Query Solutions and Techniques for All SQL Users (2nd edition, 2020) by A. Molinaro, R. de Graaf; Published by O'Reilly, isbn: 1492077445;

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

ISYS2120: Data and Information Management

Semester 2, 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