COMP SCI 7210OL - Foundations of Computer Science - Python A
Online - Online Teaching 1 - 2022
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General Course Information
Course Details
Course Code COMP SCI 7210OL Course Foundations of Computer Science - Python A Coordinating Unit Computer Science Term Online Teaching 1 Level Postgraduate Coursework Location/s Online Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Restrictions Grad Cert in Data Sci (Applied) (OL), Grad Dip in Data Sci (Applied) (OL), Master of Data Sci (Applied) (OL) OR Grad Cert in Cyber Security (OL), Grad Dip in Cyber Security (OL) OR Master of Cyber Security (OL)Only Assessment Assignments and/or exam Course Staff
Course Coordinator: Dr Rita Garcia
Course Tutor: Dr Mohammad RezvanCourse Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
Upon completion of this course/subject, students will be able to:
1.Use the Python programming language to construct basic programs
2.Evaluate real world data using Python tools
3.Translate real world data to computer representation
4.Interpret the language of data science and programming
5.Manipulate data using Python tools to create visual data representations
6.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 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.
1-5 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.
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Learning Resources
Required Resources
Zhang, 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 Learning
This course is held online and all materials are available at: https://myuni.adelaide.edu.au/courses/48836 -
Learning & Teaching Activities
Learning & Teaching Modes
This course is taught entirely online with weekly meetings with tutor.Workload
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 Summary
each week of the six weeks, learning activities follow the pattern:- Intro video
- Lessons and practice online, text readings
- Online tutor session
- Further lessons and practice online, text readings
- Research and Reflection Discussion (topics related to project)
- Peer Review
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Assessment
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 Summary
Weekly programming practicals: 30%
Weekly concept quizzes: 20%
Project: 50% (project is a hurdle requirement and students must receive a 50P or higher on the project to pass the course)Assessment Detail
Assessment 1 - A (practical assignments)
In this assessment, you will be required to demonstrate your ability to apply what you have learnt each week in the creation of programs to solve a problem.
Due date: Sunday 11:59pm end of each week.
Percentage of grade: 30%
Assessment 1 - A (online quizzes)
In this assessment, you will be required to demonstrate your knowledge of the concepts, structure, and application of the code you used in your practical work.
Due date: Start of week Tuesday 11:59pm.
Percentage of grade: 20%
Assessment 2 - (project)
In this assessment, you will be required to identify a data set to work with (either your own or one of the recommended data sets) and build a Python program to extract and visualise information about the data set. The purpose of this assessment is for you to demonstrate your ability to apply what you have learned throughout the course in the creation of a document including programs to answer questions about data and a video explaining your work.
Due date: Sunday 11:59 pm end of week 6
Percentage of grade: 50%Submission
All submissions must be made through MyUni. Follow the assessment links for each assessment item.Course Grading
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 .
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Student Feedback
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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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)
Email: info@assureprograms.com.au
Go to the 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)
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
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鈥檚 disciplinary procedures.
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