COMP SCI 7211OL - Foundations of Computer Science - Python B

Online - Online Teaching 3 - 2022

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.

  • General Course Information
    Course Details
    Course Code COMP SCI 7211OL
    Course Foundations of Computer Science - Python B
    Coordinating Unit 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
    Assessment Assignments and/or exam
    Course Staff

    Course Coordinator: Dr Rita Garcia

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. 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.

  • 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 in MyUni
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This 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 Summary
    Each 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
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    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
    1.1 Abstraction
    1.2 Design

    2. Professional Knowledge
    2.1 Ethics
    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
    3.3 Networking

    4. Technology Building
    4.1 Programming
    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 Requirements
    You 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.
    Assessment Detail

    No information currently available.

    Submission details and the assignment descriptions will be published on the course website in
    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 Access Adelaide.

  • 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 ( 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.

  • Student Support

    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
  • 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’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.