COMP SCI 7210 - Foundations of Computer Science A
North Terrace Campus - Semester 2 - 2023
General Course Information
Course Code COMP SCI 7210 Course Foundations of Computer Science A Coordinating Unit School of Computer Science Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 6 hours per week Available for Study Abroad and Exchange Incompatible COMP SCI 7202, COMP SCI 7208, COMP SCI 7103 Course Description This course will develop your coding and problem-solving skills with a focus on data and data science. You will learn fundamental programming concepts such as data, selection, iteration, functional decomposition, data organisation as well as how to apply these programming fundamental knowledge to practical problems. You will build fundamental software development skills including the use of the Python programming language and tools, debugging, object-oriented design, basic data structures, and fundamentals of good programming practice, style and design.
Course Coordinator: Dr Tim Chen
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesUpon completion of this course/subject, students will be able to:
1 Interpret and decompose problems in computational domains. 2 Justify and demonstrate an understanding of programming fundamentals. 3 Apply programming fundamental knowledge to practical problems. 4 Use the Python programming language to construct programs to solve real-world problems. 5 Independently find and interpret discipline-related documentation. 6 Explain the benefits of object-oriented design and understand when it is an appropriate methodology to use. 7 Design object-oriented solutions for small systems involving multiple objects. 8 Translate real-world data to computer representation using different data structures.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency
Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Recommended ResourcesZhang, Y., (2015), An Introduction to Python and Computer Programming,(1st ed. Lecture Notes in Engineering 353), Springer, London.
Lee, K., & Mackie, I., (2014), Python Programming Fundamentals (2nd ed. Undergraduate Topics in Computer Science), Springer, London.
Matthes, Eric. Python crash course: a hands-on, project-based introduction to programming.
Phillips, D. (2015). Python 3 object-oriented programming. Packt Publishing Ltd.
Baka, B. (2017). Python Data Structures and Algorithms. Packt Publishing Ltd.
Lee, K. D., Lee, K. D., & Steve Hubbard, S. H. (2015). Data Structures and Algorithms with Python. Springer.
Textbooks are available to students as e-books through the Library.
Online LearningAll materials are available from MyUni and it is possible to work through most of the course activities off-site.
Workshops will be conducted using Zoom for online students.
Learning & Teaching Activities
Learning & Teaching ModesThis course is structured around supported active learning labs. Assessment, discussions and support occur face to face. The materials are available online. Attendance at all sessions is not mandatory and can be negotiated with course coordinator during the first week.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
This course assumes a commitment of 20-25 hours per week over 6 weeks.
Learning Activities Summaryeach week of the six weeks, learning activities follow the pattern:
Lessons, practice (in class or in own time) and text readings
Face to Face discussion session and Peer sharing
Further lessons, practice (in class or in own time) and text readings
Research and Reflection (progress report related to project)
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Weighting (%) Individual/
Due (Week)* Hurdle criteria Learning outcomes CBOK Alignment** Programming Practice 0% Individual
Weekly 1 - 6 1.1 1.2 2.2 2.6 3.1 3.2 3.3 4.1 4.2 4.3 Module Tests 100% Individual Summative 4, 7, 10 & 13 85% 1 - 6 1.1 1.2 2.2 2.6 3.1 3.2 3.3 4.1 4.2 4.3 Total 100%
* The specific due date for each assessment task will be available on MyUni.
In accordance with the Assessment for Coursework Programs Policy, Procedure 1b: An exemption from the stated hurdle requirements has been granted.
This course has a hurdle requirement. Meeting the specified hurdle criteria is a requirement for passing the course.
**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:
1. Problem Solving
2. Professional Knowledge
2.2 Professional expectations
2.3 Teamwork concepts & issues
2.4 Interpersonal communications
2.5 Societal issues
2.6 Understanding of ICT profession
3. Technology resources
3.1 Hardware & Software
3.2 Data & information
4. Technology Building
4.2 Human factors
4.3 Systems development
4.4 Systems acquisition
5. ICT Management
5.1 IT governance & organisational
5.2 IT project management
5.3 Service management
5.4 Security management
Assessment Related RequirementsYou must complete 4 specific modules as prescribed by your program of study.
Each module has a hurdle requirement, which is the module test. You need to achieve at least 85% on the module test to pass the module. You will have a limited opportunity to retake module tests that you do not pass in subsequent test weeks but these will be arranged in conjunction with the course coordinator in later testing weeks. If you don’t pass enough of the module tests, you may be required to take any or all of the modules again in a subsequent offering.
You will be required to demonstrate your ability to apply what you have learnt each week in the creation of programs to solve practice problems to be eligible to sit for the module test.
Successful completion of an appropriate set of modules will result in a Non-Graded Pass (NGP) in this course.
No information currently available.
No information currently available.
Grades for your performance in this course will be awarded in accordance with the following scheme:
GS8 (Coursework Grade Scheme) Grade Description CN Continuing FNS Fail No Submission NFE No Formal Examination F Fail NGP Non Graded Pass P Pass C Credit D Distinction HD High Distinction RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
- Academic Support with Maths
- Academic Support with writing and speaking skills
- Student Life Counselling Support - Personal counselling for issues affecting study
- International Student Support
- AUU Student Care - Advocacy, confidential counselling, welfare support and advice
- Students with a Disability - Alternative academic arrangements
- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
- LinkedIn Learning
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
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