Note: This unit is an archived version! See Overview tab for delivered versions.
INFO1903: Informatics (Advanced) (2013 - Semester 1)
| 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
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| 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
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| Timetable: | INFO1903 Timetable | |||||||||||||||
| Time Commitment: |
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| 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: |
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| 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 |
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| Grading: |
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| 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.
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| 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 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.
