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FIT3139 Computational science - Semester 2, 2014

The unit provides an overview of computational science and an introduction to the central methods in this field. While it is not tied to any particular field of scientific study, it requires a general scientific background at advanced introductory level.

Topics include: the role of computational tools and methods in 21st century science; modelling and simulation; continuous vs discrete models; analytic versus numeric models; deterministic versus stochastic models; Monte-Carlo methods; epistemology of simulations; visualisation; high-dimensional data analysis; optimisation; limitations of numerical methods; high-performance computing and data-intensive research.

Each topic area will be introduced with a general overview followed by a discussion of one or a few selected methods in full technical detail. These will be practiced in tutorials and laboratories, which will also acquaint the students with standard software packages for scientific computing (for example, Mathematica, Matlab, Maple, Sage).

Seminars and guest lectures will present case studies and link to current topics in research.

Applications examples will be drawn from Physics, Biology, Bioinformatics, Chemistry, Social Science, etc.

Mode of Delivery

Clayton (Day)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 3-hour laboratory
  • One 1-hour tutorial/seminar

(b.) Additional requirements (all students):

  • A minimum of 6 hours independent study per week for completing lab and assignment work, private study and revision.

Additional workload requirements

Note: MURPA seminars and tutorials alternate weekly. However, due to speaker availability for MURPA seminars, there may be a slight change in the sequence of weeks the class is required to attend the MURPA seminars. The exact sequence will be made available closer to the start of the semester.

Unit Relationships


One of MAT1841, MAT2003, ENG1091, MTH1030, MTH1035 or equivalent plus any introductory programming unit (eg FIT1040, FIT1002, ECE2071, TRC2400, or equivalent)

Chief Examiner

Campus Lecturer


Arun Konagurthu

Consultation hours: Monday 3pm - 4pm



James Collier

Rui Chen

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Academic Overview

Learning Outcomes

Upon successful completion of the unit students will -
  • understand the role of computational tools and methods in modern science;
  • understand the process of model construction, model fitting, model verification and analysis in scientific problem solving;
  • understand the differences between the core modelling approaches (numeric versus analytic; continuous versus discrete; linear versus non-linear; deterministic versus stochastic);
  • understand the implications of choosing a particular modelling approach;
  • understand central computational methods for the analysis of models in each of these classes
  • understand the role of simulation and visualisation;
  • be introduced to at least one standard scientific software package for model construction and analysis;
  • have an general overview of high-performance techniques in scientific computing and of methods for data-intensive research (storage, archiving etc).

Unit Schedule

Week Activities Assessment
0 Download GNU Octave (see Required Resources). Familiarise with MATLAB using Octave. Links to the documentation (for both Octave and MATLAB) are given in the Reading list section below) No formal assessment or activities are undertaken in week 0
1 Introduction to Computational Science + Tute + Prac (Lab) Prac assignments (due weekly in labs)
2 Lectures on solving linear models + MURPA Seminar + Prac (Lab)  
3 Lectures on solving non-linear models + Tute + Prac (Lab)  
4 Lectures on Continuous and discrete models + MURPA Seminar + Prac (Lab)  
5 Lectures on solving ordinary differential equations + Tute + Prac (Lab)  
6 Lectures on Static and Dynamic Simulations + MURPA Seminar +Prac (Lab)  
7 Lectures on Monte Carlo Approach + Tute + Prac (Lab)  
8 Lectures on Linear Optimisation + MURPA Seminar + Prac (Lab) Assignment 1 due end of week 8
9 Lectures on non-linear optimisation + Tute + Prac (Lab)  
10 Lectures on data analysis + MURPA Seminar + Prac (Lab)  
11 High dimensional data analysis and visualization + Tute + Prac (Lab) Assignment 2 due end of week 11
12 High dimensional data visualization+ MURPA Seminar + Prac (Lab)  
  SWOT VAC No formal assessment is undertaken in SWOT VAC
  Examination period LINK to Assessment Policy:

*Unit Schedule details will be maintained and communicated to you via your learning system.

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.

Students first encounter the information at lectures, discuss and explore them at length during tutorials, and practice them in a hands-on environment during labs.

Assessment Summary

Examination (3 hours): 75%, In-semester assessment: 25%

Assessment Task Value Due Date
Assignment 1 10% (Part 1 = 5%, Part 2 = 5%) end of week 8
Assignment 2 10% end of week 11
Active participation in labs and tutes 5% Weekly in Labs
Examination 1 75% To be advised

Assessment Requirements

Assessment Policy

Assessment Tasks


Students are expected to  attend at least 4 out of the 6 MURPA seminars. 

Students are expected to actively participate in 4 out of the 6 tutorials. Participation includes contributing opinions to a discussion, providing an answer to questions/exercises, or posing a unit related question.

Students are expected to attend at least 8 out of the 12 pracs sessions (labs).

Note: 5% of the unit assessment is given for active participation in tutes and labs.

Failure to meet these expectations may cause difficulties in passing the unit.

  • Assessment task 1
    Assignment 1
    This assignment is about computational problem solving and has two parts.

    Part 1 - The assignment will consist of questions and problems designed to evaluate your understanding of the material provided during the first six weeks of the semester.

    Part 2 - Additionally, a short paper discussing a computational technique will be provided. The students are required to read the paper and summarise it. They will also be expected to write a MATLAB program that implements the technique.
    10% (Part 1 = 5%, Part 2 = 5%)
    Criteria for assessment:

    Part 1 - Ability to answer the questions and solve the stated problems correctly

    Part 2 - Ability to read and clearly summarise the computational technique, ability to code the technique, and correctness of the program on sample data sets

    Due date:
    end of week 8
  • Assessment task 2
    Assignment 2
    Problems will be given relating to the material learnt in the first 10 weeks of the semester.
    Criteria for assessment:

    Demonstrate abilities in modelling the problems and using the right computational/simulation techniques to solve them

    Due date:
    end of week 11
  • Assessment task 3
    Active participation in labs and tutes
    Each week you will need to complete a prac assignment using MATLAB independently. NOTE: MATLAB WILL NOT BE TAUGHT IN LECTURES. Students are required to self-learn MATLAB. GNU-Octave (see Required Resources section above) is a freely available MATLAB-like programming environment. Students should use GNU-Octave during self-study for preparation and solving the prac assignments. Pointers where needed will be provided in lectures and tutorials.

    Prac assignments are composed of several computer-based problems. The solutions to the problems are expected to be primarily designed and developed (using GNU Octave) during your 6 hours of "self-study" period every week. This means that you must have a significant proportion of the prac prepared and sorted out before attending the scheduled computer lab and testing it on the available MATLAB environment.

    The aim of the computer lab practical is to iron out any bugs, ask any questions about the prac you have not been able to solve on your own, and improve the parts that your demonstrator points out as lacking (including comments, algorithms, etc). If you do nothing before the scheduled labs,  you will soon realise that you do not have enough time to complete the assignment. The prac sheets will be released (every Thursday) and made available on the unit's web page on Moodle.

    Your will be asked to handover your work scripts/files to your demonstrator. 5% of the unit assessment goes towards your active participation in the labs. Feedback to your lab exercises will be provided during the lab hours as well as during consultation with the lecturer (where necessary). 
    Criteria for assessment:

    Active participation in labs and tutes

    Due date:
    Weekly in Labs


  • Examination 1
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:

Learning resources

Reading list

1. Scientific Computing: An Introductory Survey (second edition) Michael T. Heath. Publisher: McGraw-Hill

2. Introduction to Computational Science: Modelling and Simulation for Sciences. Angela B. Shiflet and George W. Shiflet. Publisher: Princeton University Press

3. Applied Numerical Methods with MATLAB for Engineers and Scientists. Steve C Chapra, McGraw-Hill

4. Insight Through Computing: A MATLAB introduction to Computational Science and Engineering. Charles F. Van Loan and K.-Y. Daisy Fan

5. Computational Science and Engineering. Gilbert Strang Publisher: Wellesley-Cambridge Press

6. Getting started with MATLAB: A Quick introduction for scientists and Engineers. Rudra Pratap. Publisher: Oxford University Press

7. Wiki resource on GNU Octave: 8. MATLAB documentation:

Monash Library Unit Reading List (if applicable to the unit)

Faculty of Information Technology Style Guide

Feedback to you

Examination/other end-of-semester assessment feedback may take the form of feedback classes, provision of sample answers or other group feedback after official results have been published. Please check with your lecturer on the feedback provided and take advantage of this prior to requesting individual consultations with staff. If your unit has an examination, you may request to view your examination script booklet, see

Types of feedback you can expect to receive in this unit are:

  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Test results and feedback
  • Solutions to tutes, labs and assignments

Extensions and penalties

Returning assignments

Assignment submission

It is a University requirement ( for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz). Please note that it is your responsibility to retain copies of your assessments.

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning site for this unit, which you can access via links in the portal.

Required Resources

Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.

MATLAB programming environment will be used in Pracs. However, since MATLAB is not freely available students should use GNU Octave (a freely available MATLAB-like numerical programming language) for self-study. GNU Octave source as well as binaries (for various operating systems) can be downloaded from this link:

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