COMP SCI 7211OL - Foundations of Computer Science - Python B
Online - Online Teaching 1 - 2023
General Course Information
Course Code COMP SCI 7211OL Course Foundations of Computer Science - Python B Coordinating Unit School of 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 Prerequisites Carousel 1 Courses: COMP SCI 7212OL, COMP SCI 7210OL, DATA 7201OL & DATA 7202OL or MATHS 7203OL 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 Weitong Chen
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.
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 SummaryWeekly 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 DetailAssessment 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%
No information currently available.
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.
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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)
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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- Student Experience of Learning and Teaching Policy
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