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INFO1903: Informatics (Advanced) (2013 - Semester 1)

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Unit: INFO1903: Informatics (Advanced) (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Junior
Faculty/School: School of Computer Science
Unit Coordinator/s: Prof Fekete, Alan
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit: http://www.it.usyd.edu.au/~info1903
Campus: Camperdown/Darlington
Pre-Requisites: ATAR sufficient to enter BCST(Adv), BIT or BSc(Adv), or portfolio of work suitable for entry
Brief Handbook Description: This unit covers advanced data processing and management, integrating the use of existing productivity software, e.g. spreadsheets and databases, with the development of custom software using the powerful general-purpose Python scripting language. It will focus on skills directly applicable to research in any quantitative domain. The unit will also cover presentation of data through written publications and dynamically generated web pages, visual representations and oral presentation skills. The assessment, a semester long project, involves the demonstration of these skills and techniques for processing and presenting data in a choice of domains.
Assumed Knowledge: None.
Lecturer/s: Prof Fekete, Alan
Timetable: INFO1903 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Tutorial 3.00 1 13
2 Lecture 3.00 1 13
T&L Activities: Lecture: Three lectures per week. One lecture every week is an interactive Python lecture in which the lecturer demonstrates programming on the fly, taking questions from the class.

There is a 5 minute activity break in the middle of each lecture that covers non assessable material such as back of the envelope exercises.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Identify, define and analyse problems that require computational solutions;

Select suitable tools and techniques to solve computational problems and justify your choice in terms of their strengths and limitations;		 Design (Level 4)
Approach further learning in terms of the core principles of IT so that you can adapt to rapidly developing information technologies;

Write correct, elegant Python programs to manipulate data; Read and interpret Python code and documentation;

Develop, test and debug software in a systematic manner; Understand the fundamentals of object oriented programming.

Understand data representation in computer systems.		 Engineering/IT Specialisation (Level 3)
Make sensible quantitative estimates (back of the envelope calculations). Maths/Science Methods and Tools (Level 2)
Use spreadsheets to solve numerical problems; Understand the relational model and query relational databases with SQL; Information Seeking (Level 4)
Present information effectively in verbal, written and graphical forms using standard software tools; Communication (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Design (Level 4)
1. Identify, define and analyse problems that require computational solutions;
2. Select suitable tools and techniques to solve computational problems and justify your choice in terms of their strengths and limitations;
Engineering/IT Specialisation (Level 3)
3. Approach further learning in terms of the core principles of IT so that you can adapt to rapidly developing information technologies;
4. Write correct, elegant Python programs to manipulate data; Read and interpret Python code and documentation;
5. Develop, test and debug software in a systematic manner; Understand the fundamentals of object oriented programming.
6. Understand data representation in computer systems.
Maths/Science Methods and Tools (Level 2)
7. Make sensible quantitative estimates (back of the envelope calculations).
Information Seeking (Level 4)
8. Use spreadsheets to solve numerical problems; Understand the relational model and query relational databases with SQL;
9. Write Unix pipelines to manipulate textual data; Understand web technology and develop web-based user interfaces.
Communication (Level 3)
10. Present information effectively in verbal, written and graphical forms using standard software tools;
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Mid-Sem Exam No 10.00 Week 6 4, 5, 6, 7, 8, 9,
2 Assignment No 5.00 Week 4 1, 8, 9,
3 Assignment No 10.00 Week 10 1, 2, 4, 5, 8,
4 Assignment No 15.00 Week 12 3, 4, 5, 8, 9, 10,
5 Oral Presentation and Handout No 10.00 Week 13 3, 10,
6 Final Exam No 50.00 Exam Period 1, 2, 4, 5, 6, 7, 8, 9, 10,
Assessment Description: Mid-Sem Exam: Mid-semester prac test

Assignment: Major project - stage 1: data processing

Assignment: Major project - stage 2: create database

Assignment: Major project - stage 3: dynamic web front-end

Report: Major project - oral presentation and handout

Final Exam: Final exam
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of IT may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Other policies

All university policies can be found at http://sydney.edu.au/policy

Policies and request forms for the Faculty of Engineering and IT can be found on the forms and policies page of the faculty website at http://sydney.edu.au/engineering/forms
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
  • Learning Python
Online Course Content: http://www.it.usyd.edu.au/~info1903
Note on Resources: Lecture notes, tutorial notes and online questions will be provided.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction to data, Python
Week 2 Regular expressions, spreadsheets, Python
Week 3 Spreadsheets, databases, Python
Week 4 Databases, SQL, Python
Assessment Due: Assignment
Week 5 SQL, Python
Week 6 Internet technologies, Python
Assessment Due: Mid-Sem Exam
Week 7 CGI scripting, Python
Week 8 Software engineering
Week 9 XML, Web services, Python
Week 10 Information visualisation, Python
Assessment Due: Assignment
Week 11 Communication, Python
Week 12 Data management, Java for Python programmers
Assessment Due: Assignment
Week 13 Advanced IT seminars, Review material
Assessment Due: Oral Presentation and Handout
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Bachelor of Computer Science and Technology 2015, 2016, 2017, 2025
Aeronautical Engineering / Science 2011, 2012, 2013
Aeronautical Engineering (Space) / Science 2011, 2012, 2013, 2014
Biomedical Engineering / Science 2013, 2014
Chemical & Biomolecular Engineering / Science 2011, 2012, 2013, 2014
Civil Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Bioelectronics) / Science 2011, 2012
Electrical Engineering / Science 2011, 2012, 2013, 2014
Electrical Engineering (Computer) / Science 2014
Electrical Engineering (Power) / Science 2011, 2012, 2013, 2014
Electrical Engineering (Telecommunications) / Science 2011, 2012, 2013, 2014
Aeronautical (Space) / Science 2015
Biomedical /Science 2015, 2016, 2017
Chemical & Biomolecular / Science 2015, 2016, 2017
Civil / Science 2015, 2016, 2017
Electrical / Science 2015, 2016, 2017
Mechanical / Science 2015, 2016, 2017
Mechanical (Space) / Science 2015
Mechatronic / Science 2015, 2016, 2017
Mechatronic (Space) / Science 2015
Software / Science 2015, 2016, 2017
Mechanical Engineering (Biomedical) / Science 2011, 2012
Mechanical Engineering / Science 2011, 2012, 2013, 2014
Mechanical Engineering (Space) / Science 2011, 2012, 2013, 2014
Mechatronic Engineering / Science 2011, 2012, 2013, 2014
Mechatronic Engineering (Space) / Science 2011, 2012, 2013, 2014
Project Engineering and Management (Civil) / Science 2011
Software Engineering / Science 2011, 2012, 2013, 2014
Software Engineering / Law 2014
Flexible First Year / Science 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
Flexible First Year (Stream A) / Science 2012, 2013, 2014
Flexible First Year (Stream B) / Science 2012, 2013, 2014

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Design (Level 4) Yes 17.5%
Engineering/IT Specialisation (Level 3) Yes 30.5%
Maths/Science Methods and Tools (Level 2) Yes 6%
Information Seeking (Level 4) Yes 30.5%
Communication (Level 3) Yes 15.5%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.