COMP SCI 7211OL - Foundations of Computer Science - Python B
Online - Online Teaching 3 - 2022
General Course Information
Course Code COMP SCI 7211OL Course Foundations of Computer Science - Python B Coordinating Unit School of Computer Science Term Online Teaching 3 Level Postgraduate Coursework Location/s Online Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Prerequisites Carousel 1 Courses: COMP SCI 7212OL, COMP SCI 7210OL, DATA 7201OL & DATA 7202OL Incompatible COMP SCI 7202OL Assumed Knowledge Assumed knowledge programming experience as would be gained from COMP SCI 7210OL. Restrictions Graduate Diploma in Data Science (Applied) OL OR Master of Data Science (Applied) OL Only Course Description Introduces fundamental concepts of building data science applications in Python. Object oriented fundamentals ? methods, and classes. Algorithms and problem solving - problem solving processes and strategies. Computational complexity of algorithms. Software development tools and techniques - testing: black box, requirements. Representation and manipulation of large scale data sets.
Course Coordinator: Dr Rita Garcia
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. Evaluate real world problems and data and translate to computer representation.
2. Demonstrate practical ability to use Python prediction and classification tools.
3. Demonstrate ability to construct complex Python programs.
4. Interpret and express the language of data science and programming.
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 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.
Required ResourcesZhang, Y. (2015). An Introduction to Python and Computer Programming(1st ed. 2015. ed., Lecture Notes in Electrical Engineering, 353).Lee, K., & Mackie, I. (2014).
Python Programming Fundamentals(2nd ed. 2014 ed., Undergraduate Topics in Computer Science). London: Springer London.Jake VanderPlas. (2016).
Python Data Science Handbook: Essential Tools for Working with Data(1st ed.). O'Reilly Media, Inc.
Nelli, F., (2018), Python Data Analytics With Pandas, NumPy, and Matplotlib (Links to an external site.), (2nd ed.), Springer, New York.
Texts other than the "Python Data Science Handbook" are available to students as e-books through the Library. The Data Science Handbook is available through the library on a limited (short term loan) basis as an e-book or a personal copy can be purchased.
Online LearningThis course is held online and all materials are available in MyUni
Learning & Teaching Activities
Learning & Teaching ModesThis course is taught entirely online with weekly meetings with tutor.
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 study and practice commitment of 20-25 hours per week.
Learning Activities SummaryEach week of the six weeks, learning activities follow the pattern:
1. Intro video
2. Lessons and practice online, text readings
3. Online tutor session
4. Further lessons and practice online, text readings
5. Research and Reflection Discussion (topics related to project)
6. Peer Review
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/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Programming Practice 0 Individual Formative 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 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.
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
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.
SubmissionSubmission details and the assignment descriptions will be published on the course website in http://myuni.adelaide.edu.au.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination 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
Counselling for Fully Online Postgraduate Students
Fully online students can access counselling services here:
Phone: 1800 512 155 (24/7)
SMS service: 0439 449 876 (24/7)
Go to the Study Smart Hub to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)
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
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