FIT1008 Introduction to computer science - Semester 2, 2015

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 the machine models and simple assembly language programming. Topics include data types; data structures; algorithms; algorithmic complexity; recursion; and translation to assembly language.

Mode of Delivery

• Clayton (Day)
• Malaysia (Day)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

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

• Three hours of lectures
• One 1-hour tutorial
• One 3-hour laboratory

(b.) Additional requirements (all students):

• A minimum of 5 hours of personal study per week in order to satisfy the reading and assignment expectations.

See also Unit timetable information

Additional workload requirements

The One 3-hour laboratory will be a Computer Lab Prac that includes 1.5 hours per week (requiring advance preparation) followed by an extra 1.5 hours for marking.

Unit Relationships

Prohibitions

CSE1303, CSC1030, FIT1015, FIT2085

Prerequisites

(FIT1040 or ECE2071 or FIT1002) and FIT1029
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.

Chief Examiner

Campus Lecturer

Your feedback to Us

Previous Student Evaluations of this Unit

Pracs will be released earlier, to give students more time to work on them and collaborate with their prac partner.

Academic Overview

Learning Outcomes

1. implement and modify common data types such as stacks, queues, lists, trees, priority queues, heaps and hash tables using a variety of data structures such as arrays and linked nodes. Implement simple algorithms that manipulate these data types. Construct new basic data types;
2. compare and evaluate different implementations of a basic data type and evaluate their implications regarding time complexity, functionality, and memory usage;
3. design and implement simple recursive algorithms and data structures, including those manipulating lists, trees and heaps. Assess the relationship between recursive and iterative algorithms, their advantages and disadvantages;
4. calculate the best case and worst case big O time complexity of simple iterative and recursive algorithms (including all those studied in the unit);
5. manually translate simple high level code containing if-then-elses, loops, arithmetic and function calls into the assembly code used by a particular computer architecture such as MIPS R2000.

Unit Schedule

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

Teaching Approach

Assessment Summary

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

Assessment Requirements

Assessment Policy

Assessment Tasks

Participation

Peer Instruction Participation occurs in every lecture.

• Assessment task 1

  Title:

  Mid-semester Test (1 hour)

  Description:

  Learning Outcomes: 1,2,4,5. 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:

  5%

  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 a lecture

• Assessment task 2

  Title:

  Pracs (1.5 hours each)

  Description:

  Learning Outcomes: 1,2,3,4,5. 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 first 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 prac,  you will soon realise that you do not have enough time to complete it. 

  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

• Assessment task 3

  Title:

  Student participation

  Description:

  Learning Outcomes: 2,4,5. Student participation will take place during lectures using clickers. Assessment will be carried out on a weekly basis via Moodle quizzes. 

  Weighting:

  5%

  Criteria for assessment:

  A grade will be based on a student's quizzes via Moodle. 

  Due date:

  Every week

  Remarks:

  Students are required to purchase a Turning Point clicker from the Campus Bookstore or directly from the Australian Distributor.

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:

• D. Reed and J. Zelle - Data Structures and Algorithms using Python and C++

For MIPS

• R.L. Britton - MIPS Assembly Language Programming

For learning Python 3:

• L. Perkovic - Introduction to Computing using Python: An Application Development Focus

For more Python 3 (for more experienced programmers):

• Dive into Python 3: http://getpython3.com/diveintopython3/
• 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

PyCharm: A good Python IDE. (http://www.jetbrains.com/pycharm/)

MARS (MIPS Assembler and Runtime Simulator): An IDE for MIPS Assembly Language Programming. (http://courses.missouristate.edu/kenvollmar/mars/)

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

Additional subject costs

Students are required to purchase a Turning Point clicker from the Campus Bookstore or directly from the Australian Distributor. More information about clickers can be found on this webpage: http://intranet.monash.edu.au/infotech/resources/students/enrolment/clickers.html.

Other Information

Policies

Graduate Attributes Policy

Student Charter

Student services

Monash University Library

Disability Liaison Unit

Other

Engineers Australia Stage 1 competencies

This unit is a core unit in the Bachelor of Software Engineering accredited by Engineers Australia. Engineers Australia Accreditation Policy of Professional Engineering Programs requires that programs demonstrate how engineering graduates are prepared for entry to the profession and achieve Stage 1 competencies. The following information describes how this unit contributes to the development of these competencies for the Bachelor of Software Engineering. (Note: not all competencies may be emphasised in this unit).

Relationship between Unit Learning Outcomes and BSE Course Outcomes

Relationship between Unit Learning Outcomes and Assessments