COMP SCI 1012 - Scientific Computing

North Terrace Campus - Semester 1 - 2017

This course provides an introduction to basic computer programming concepts and techniques useful for Scientists, Mathematicians and Engineers. The course exposes students to practical applications of computing and commonly used tools within these domains. It introduces techniques for problem solving, program design and algorithm development. MATLAB (approximately 24 lectures): Basic programming: introduction to the MATLAB environment and the MATLAB help system, data types and scalar variables, arithmetic and mathematical functions, input and output, selection and iteration statements. Functions: user defined functions, function files, passing information to and from functions, function design and program decomposition, recursion. Arrays: vectors, arrays and matrices, array addressing, vector, matrix and element-by-element operations. Graphics: 2-D and 3-D plotting. Mathematical modelling: dynamical systems, linear systems, numerical differentiation and integration. Spreadsheets (approximately 6 lectures): Spreadsheets as a tool for Scientific Computing: calculation, using in-built functions, plotting and fitting, modelling and optimisation using the Goal-Seek and Solver tools, data analysis.

  • General Course Information
    Course Details
    Course Code COMP SCI 1012
    Course Scientific Computing
    Coordinating Unit Computer Science
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 5 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites SACE stage 2 Mathematical Studies or equivalent
    Incompatible APP MTH 1000, APP MTH 2106, ENG 2002, CHEM ENG 1002 & APP MTH 2005
    Assessment written exam, assignments
    Course Staff

    Course Coordinator: Professor David Suter

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:

    1 Write simple Matlab program scripts.
    2 Solve systems of linear equations using Matlab.
    3 Find roots of of mathematical functions.
    4 Numerically solve simple differential equations.
    5 Find optimum solutions to numerical problems.
    6 Use Monte-Carlo techniques to obtain approximate solutions.
    7 Explain the mathematical basis of the above techniques.

    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
  • Learning Resources
    Required Resources
    There is no required text-book. Comprehensive lecture notes (300+ pages) will be made available online from the course website as PDF files.
    Recommended Resources
    Essential MATLAB for Engineers and Scientists by Brian Hahn and Daniel Valentine.
    Online Learning
    There is an online forum, managed by MyUni. We will use the forum to announce all changes to the course, exercises, and tutorials, and to answer/address questions. 
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be taught using a variation of team-based learning, with lecture/demonstration sessions possibly interspersed with quiz/question time sessions. There will also be tutorial classes, and supervised practical sessions.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Scientific Computing is a three-unit course. We expect that you will spend between 9-12 hours each week working on the course. This will consist of:
    • 3 hours of lectures per week.
    • 1 tutorial in the even weeks for 3 weeks, plus 1 hour of tutorial preparation. 1 SGDE session fornightly after that, plus 1 hour of out-pf-session discussion.
    • 2 hours of supervised practical work per week for 10 weeks.
    • 5-7 hours a week spent completing practical assignments. The work spent on practical assignments is likely to be closely associated with an assignment deadline, rather than spread out evenly across the semester. You should allow yourself enough time to complete the assignments to a high level.
    Learning Activities Summary
    The planned timetable of lectures can be found at the online course forum.
    Specific Course Requirements
    You must obtain 40% of the marks in the final exam, or your overall final mark will be capped at 44F. A minimum score of 50% overall is required to pass the course.
    Small Group Discovery Experience
    There will be three SGDE sessions, held fortnightly, in the second half of the course.
  • 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 for the course consists of three components: Weekly supervised practical exercises (35% total), SGDE sessions (5% total), and a final exam (60%).

    The due date for each exercise is published on the course website, and in each practical exercise specification. You must submit your exercises by the specified deadline, or you may suffer a reduction in your final marks. If you have a very good reason for extension, apply to the lecturer. Requests must be made in advance (unless you were unconscious or incapacitated), or they may not be granted.
    Assessment Related Requirements
    You must obtain at least 40% of the marks in the exam, or your overall final mark will be capped at 44F. A minimum score of 50% overall is required to pass the course.
    Assessment Detail
    All practical exercises involve writing programs, either in Matlab or using Excel. You are strongly encouraged to begin the exercises early, and to test your own program thoroughly.
    All practical assignments must be submitted through the Scientific Computing web-page. Instruction on how to do this are in the specification for each exercise.
    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
  • 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.

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