Outline

Overview

This unit is an essential starting point for software developers, IT consultants, and computer scientists to build their understanding of principle computer operation. Students will obtain knowledge and skills with procedural programming. Crucial concepts include defining data types, control flow, iteration, functions, recursion, the model of addressable memory. Students will be able to reinterpret a general problem into a computer problem, and use their understanding of the computer model to develop source code. This unit trains students with software development process, including skills of testing and debugging. It is a prerequisite for more advanced programming languages, systems programming, computer security and high performance computing.

Unit details and rules

Unit code	INFO1110
Academic unit	Computer Science
Credit points	6
Prohibitions ?	INFO1910 or INFO1103 or INFO1903 or INFO1105 or INFO1905 or ENGG1810
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Vera Chung, vera.chung@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Examination 2 hours computer exam.	65%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Solve computer programming problems before due date.	15%	Week 05 Due date: 11 Apr 2021 at 23:59	14 days
Outcomes assessed:
Assignment	Assignment 2 Write a complete program from a problem description	20%	Week 12 Due date: 24 May 2021 at 23:59	14 days
Outcomes assessed:
= Type B final exam ?

Assessment summary

Assignment 1 and assignment 2:
Several programming problems to be completed individually throughout the semester. Both assignments may require submission of code, tests, reports, oral presentations. Format and submission details of assignments to be provided on release.

Final examination:
The final exam covers all aspects of the course. Demonstrate knowledge in procedural programming. Reading and tracing through short programs. Writing short programs. Writing test cases and debugging with existing test cases. The final exam is a 2 hours computer examination during the examination period.

Detailed information for each assessment can be found on the course website: edstem.org

Special consideration
Assignment 1 and 2 - Approved special consideration will be granted an extension to complete the assignment and additionally be examined in an oral quiz online, based on the assignment contents, that will contribute to the grade of the assignment.
Final examination – a replacement examination will be arranged, however, the format may vary and further development may be required.

Conditions for the pass in this course:
- At least 40% in the assessment grade
- At least 40% in the computer examination
- At least 50% total

It is a policy of the School of Computer Science that in order to pass this unit, 40% in the final exam. 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.

Additionally, for this course, students may be asked for further development of their assessments if they fail to attend at least 80% of their tutorials.

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.

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

The tutor will provide feedback for the weekly Lesson during the tutorial.

Problem sets require online submission by the due date for checking by teaching staff.

The tutors will provide further feedback to students about correctness, style and testing.

Automatically graded submissions provide further feedback.

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 penalty for any online assessment is 25% per day. It is a cap based penalty: 1 day late, maximum attainable mark is 75%. 2 days late, maximum attainable mark is 50%. 3 days late, maximum attainable mark is 25%.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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	1. Introduction to the unit; 2. Fundamental concepts; 3. First program.	Lecture (2 hr)
Week 01	1. Programming basics; 2. Online face to face.	Computer laboratory (2 hr)
Week 02	1. Data types; 2. Variables, operators and expressions. 3. Branching	Lecture (2 hr)
Week 02	1. Data types; 2. Expressions and variables; 3. The underlying memory model.	Computer laboratory (2 hr)
Week 03	1. Control Flow: Branching, arrays and loops	Lecture (2 hr)
Week 03	Conditionals and loops	Computer laboratory (2 hr)
Week 04	1. Arrays and addressable memory; 2. Further flow control; 3. Simple idioms; 4. Testing.	Lecture (2 hr)
Week 04	1. defining and iterating arrays; 2. collections of objects.	Computer laboratory (2 hr)
Week 05	1. Functions; 2. Basis of program design process, Documentation and style. 3. Testing	Lecture (2 hr)
Week 05	1. Functions,	Computer laboratory (2 hr)
Week 06	1. Files and basic input/outpu; 2. Exception handling 3. Testing	Lecture (2 hr)
Week 06	1. Working with file input and output; 2. Exceptions.	Computer laboratory (2 hr)
Week 07	1. Testing; 2. Software design process	Lecture (2 hr)
Week 07	1. Writing tests; 2. Testing programs	Computer laboratory (2 hr)
Week 08	1. Programming idioms,	Lecture (2 hr)
Week 08	1. Searching collections; 2. Error handling; 3. Further idioms	Computer laboratory (2 hr)
Week 09	1. Modular programming; 2. Defining datatypes using aggregate structures.	Lecture (2 hr)
Week 09	Creating and working with aggregate data structures and their operations.	Computer laboratory (2 hr)
Week 10	1. Idioms; 2. Testing and Debugging.	Lecture (2 hr)
Week 10	Test driven development and debugging.	Computer laboratory (2 hr)
Week 11	1. Recursion	Lecture (2 hr)
Week 11	1. Reading, writing and tracing recursive code	Computer laboratory (2 hr)
Week 12	1. Data types and multidimensional arrays	Lecture (2 hr)
Week 12	1. Defining and application of multidimensional arrays	Computer laboratory (2 hr)
Week 13	1. Course review; 2. Final examination overview;	Lecture (2 hr)
Week 13	1. Revision.	Computer laboratory (2 hr)

Attendance and class requirements

Course websites:

The course website on edstem.org will contain information, including important announcements. Teaching staff will be communicating to all students and it is considered part of the course. Students are expected to regularly visit this website to know these announcements and information concerning format and schedule of assessment. Canvas is a website that will be used to disseminate the online lecture recordings and for publishing of results.

In person vs online attendance:

Students are asked to attend their tutorial class each week as part of their study. The university has allowed students the choice between attending on campus or online. In either case, students are advised to follow the procedures concerning changing attendance, or late attendance, or failure to attend. Such procedures will be presented in the course lectures.

About the seminar:

Attendance is not compulsory
It will not contain new content needed to complete this course
Seminar has value to those who need more help with reviewing material and programming exercises

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

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Robert Sedgewick, Kevin Wayne, Robert Dondero – Introduction to Programming in Python: An Interdisciplinary Approach. Pearson Higher Ed USA, 2015. 9780134076430

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.

At the completion of this unit, you should be able to:

LO1. employ programming style conventions for writing consistently readable code
LO2. design and construct new functionality to existing procedural program or function
LO3. compose a structured algorithmic design to solve the descriptive problem specification
LO4. compose an entire procedural program from descriptive problem specification
LO5. demonstrate an understanding of programming principles, data types, variables and operators, control-flow: simple statement, sequence, if-then-else, while, functions: stack, input/output, reference memory model
LO6. compose, analyse and trace procedural code, scoping/variable lifetime, memory of the stack, references and globals, data types, operations on data types
LO7. construct code cliches for input and manipulating arrays, including maximum, minimum, search or traverse, with actions on each element for counting or summation
LO8. construct and assess code for recursively-defined numerical functions, and for recursively described array manipulations
LO9. apply testing methods and assess programs through debugging with the ability to write a set of tests for a small program or function
LO10. explain compilation process and debugging mechanism
LO11. use standard library functions

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

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains.
	Engineers Australia Curriculum Performance Indicators - 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.3. Creativity and innovation.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 3.3. Creativity and innovation. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.1. An ability to communicate with the engineering team and the community at large.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.3. Creativity and innovation.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.1. An ability to communicate with the engineering team and the community at large. 3.2. Information literacy and the ability to manage information and documentation. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.7. Proficiency in appropriate laboratory procedures; the use of test rigs, instrumentation and test equipment. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 3.1. An ability to communicate with the engineering team and the community at large.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 3.1. An ability to communicate with the engineering team and the community at large.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Students endorsed having weekly activities. They found them more rewarding for their learning and allowed them to keep up with the course. We have re-introduced them.

Additional information

Every week students should:

Read the required sections of literature
Attend and take notes for the Live lecture (Mondays)
Watch and take notes for the Online lecture (via Canvas website)
Complete the weekly Lesson for Lecture
Prepare for the Tutorial by reviewing reading, lecture and tutorial questions
Attend and participate in weekly Tutorial with tutor (as timetabled)

Additionally:

Students should ask questions on edstem.org
Students are encouraged to attend and/or watch the OPTIONAL seminar

Access to course contents: Lecture, Tutorial, Assessments Access to important announcements, discussion and support forum.

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

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

INFO1110: Introduction to Programming

Semester 1, 2021 [Normal day] - Remote

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

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect