FIT1008 Introduction to computer science - Semester 2, 2013

This unit introduces students to core problem-solving, analytical skills, and methodologies useful for developing flexible, robust, and maintainable software. In doing this it covers a range of conceptual levels, from high level algorithms and data-structures, down to abstract machine models and simple assembly language programming. Topics include data structures; algorithms; object-oriented design and programming; and abstract machines.

Mode of Delivery

• Clayton (Day)
• Sunway (Day)

Contact Hours

3 hrs lectures/wk, 3 hrs laboratories/wk, 1 hr tutorial/wk

Workload requirements

Students will be expected to spend a total of 12 hours per week during semester on this unit as follows:

• Lectures: 3 hours per week
• Tutorial: 1 hour per week
• Computer Lab Prac: 1.5 hours per week (requiring advance preparation) followed by an extra 1.5 hours for prac marking

• a minimum of 5 hours of personal study per week in order to satisfy the reading, assignment expectations, including time for newsgroups/discussion groups.

Unit Relationships

Prohibitions

CSE1303, CSC1030, FIT1015
Students beginning FIT1008 are assumed to be able to: Identify the main components of an algorithm (variables, operators, expressions, etc), and write the algorithm associated to the specification of a simple problem. Be able to translate a simple algorithm into a program containing variable declarations, selection, repetition, and lists and/or arrays.

Prerequisites

(FIT1040 or ECE2071 or FIT1002) and FIT1029

Chief Examiner

Campus Lecturer

Academic Overview

Learning Outcomes

• understand abstract data types and, in particular, data structures for stacks, queues, lists, trees, and heaps, as well as the associated algorithms for creating and manipulating them. Evaluate the appropriateness of different data structures for a given problem;
• understand basic searching and sorting algorithms, analyse their properties and be able to implement them. Understand the concept of algorithmic complexity. Analyse the complexity of these searching and sorting algorithms as well as other basic algorithms. Compare the complexity of different algorithms for solving a given problem;
• analyse different implementations of abstract data types and determine their implications regarding complexity, functionality, and memory usage;
• understand the uses of recursive algorithms and data structures, their advantages and disadvantages. Analyse the complexity of simple recursive algorithms, and their relationship with iteration. Understand basic recursive algorithms for lists and trees, and develop new ones;
• understand the different compilation targets, including abstract machine code, assembly language, object code, and machine code. Understand the relationship between simple code in a high level imperative language and its low level translation into assembly code;
• learn the structure and design of a particular processor simulator. Analyse the execution in this simulator of simple iterative algorithms learned before, thus gaining a deeper understanding of the connection between software and hardware, between an algorithm and its execution;
• understand how the simulator implements function calling, and use it to reinforce the connection between recursion and iteration.

• conform to programming standards when writing software;
• use good design principles when constructing systems;
• take a patient and thorough approach to testing;
• acknowledge any assistance they have received in writing a program;
• search for information in appropriate places when necessary.

• implement their own data-structures. Design and implement programs using a variety of data structures and algorithms;
• construct a test harness for testing a program;
• debug and modify an existing program (written by somebody else);
• use an Integrated Development Environment when developing their programs.

• document a program correctly;
• explain how parts of a program work.

Unit Schedule

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

Assessment Summary

Examination (3 hours): 70%; In-semester assessment: 30%

Teaching Approach

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

Assessment Tasks

Hurdle Requirements

There are four hurdles in this unit:

• First, students must actively participate in at least 7 out of 12 tutorials. Active participation includes contributing opinions to a discussion, providing an answer to some question/exercise, or posing a unit-related question.
• Second, students must achieve at least 50% of the total prac marks.
• Third, students must achieve at least 50% of the exam mark. 
• Fourth, students must achieve at least 50% of the total marks.

Students who do not meet all these hurdles can get a maximum of 49N for the unit.

Participation

Students must actively participate in at least 7 out of the 12 tutorials. Active participation includes contributing opinions to a discussion, providing an answer to some question/exercise, or posing a unit-related question.

• Assessment task 1

  Title:

  Mid-semester Test (1 hour)

  Description:

  This test is performed during the class and covers exam-like questions for the first part of the course. It is intended to give students an idea of how they would perform in the final exam, given their current progress. 

  Weighting:

  10%

  Criteria for assessment:

  This test will evaluate your understanding of the material provided during the first few weeks of semester, your capability to code simple algorithms given a clear specification, and to analyse the behaviour and complexity of simple fragments of code.

  Due date:

  Week 7 in Monday lecture

• Assessment task 2

  Title:

  Pracs (1.5 hours each)

  Description:

  Each week you will need to complete a prac assignment. In some of the pracs, you will need to work together with another student, but will you will be marked individually. Prac assignments are long (they are designed to take about 4 hours). This means that you must have a significant proportion of the prac completed before attending the scheduled computer lab. The aim of the 1.5 hour 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 1.5 hours scheduled,  you will soon realise that you do not have enough time to complete it. The prac sheets will be released every Thursday morning and made available on the unit's web page.

  Weighting:

  20%

  Criteria for assessment:

  Every prac sheet contains the assessment criteria used to assess that prac. In addition, demonstrators carry with them a marking guide prepared by the lecturer which indicates how exactly to mark each prac question. You can request the demonstrator to show you the marking guide after he/she has marked your prac.

  Due date:

  Weekly except in Week 7

Examinations

• Examination 1

  Weighting:

  70%

  Length:

  3 hours

  Type (open/closed book):

  Closed book

  Electronic devices allowed in the exam:

  None

Learning resources

Reading list

For algorithms and data structures:

(1) Reed and Zelle - Data Structures and Algorithms using Python and C++

(2) Rance D. Necaise - Data Structures and Algorithms using Python

For learning Python 3, beginners:

(1) How to think like a computer scientist:http://openbookproject.net/thinkcs/python/english3e/

(2) Learn Python the hard way (BUT: it is for python 2, requires reworking the examples with the notes below): http://learnpythonthehardway.org/book/

(2.1) Notes for the conversion of the exercises to Python 3: http://archive.p2pu.org/node/16646/forums/25197

(3) John Guttag -- Introduction to Computation and Programming using Python

For learning Python 3, more experienced programmers:

(1) Dive into Python 3: http://getpython3.com/diveintopython3/

(2) Python tutorial: http://docs.python.org/3/tutorial/

Feedback to you

• Informal feedback on progress in labs/tutes
• Graded assignments without comments
• Test results and feedback
• Other: Detailed solutions to tutes

Extensions and penalties

Returning assignments

Assignment submission

Online submission

Please submit your work via Moodle at the time in which you are marked (and, thus, before you leave the lab). You can access Moodle via links in the my.monash portal.

Required Resources

Python 3: This will be our programming language. Please make sure you get version 3.3

Aptana: A good IDE. It's a packaging of eclipse + PyDev plugin + extras. Requires Java to run. It is very complete but a bit heavy. Since it runs on Java, it runs for any version of Python. http://www.aptana.com

The MIPS R2000 simulator SPIM S20 (with its new interface QtSpim).

All the above are included as part of the Standard Operating Environment used in Faculty Computer Labs.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

Your feedback to Us

Previous Student Evaluations of this Unit

As a result of student feedback, we have done the following:

• Added programs with the code used in all lectures. This code gives you a good idea of what we want in terms of commenting and testing.
• Added quizzes to each week to help students practice the concepts being taught.