Note: This unit is an archived version! See Overview tab for delivered versions.
INFO1903: Informatics (Advanced) (2017 - 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: |
| 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 and decision-making in any quantitative domain. The unit will also cover presentation of data through written publications, visual representations and dynamically generated web pages. The assessment includes a semester long project, that involves the demonstration of these skills and techniques for processing and presenting data in a chosen domain. |
| Assumed Knowledge: | None. |
| Additional Notes: | Department permission required for enrolment. |
| Department Permission | Department permission is required for enrollment in this session. |
| Lecturer/s: |
Prof Fekete, Alan
Associate Professor Kummerfeld, Bob Dr Zhou, Ying |
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| Timetable: | INFO1903 Timetable | ||||||||||||||||||||
| Time Commitment: |
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| T&L Activities: | Lecture: Three lectures per week. One lecture in many weeks is an interactive Python lecture in which the lecturer demonstrates programming on the fly, taking questions from the class. Tutorial/lab work consistes of two 90-minute sessions each week, devoted to practical work and discussions (and on several weeks, to assessment tasks) |
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 3) |
| 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 2) |
| 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 2) |
| Present information effectively in verbal, written and graphical forms using standard software tools; | Communication (Level 2) |
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 3)
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 2)
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 2)
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 2)
10. Present information effectively in verbal, written and graphical forms using standard software tools;
| Assessment Methods: |
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| Assessment Description: |
* indicates an assessment task where special consideration cannot involve reweighting or substitution of other types of task. Mid-Sem Exam: Mid-semester practical test, done online during scheduled lab in week 7; covers Unix shell tools, spreadsheets and simple Python Asst 1: practical work solving tasks with Unix shell tools and spreadsheets Asst 2: practical work solving tasks with Python programming and SQL Project - stage 1: finding data from a domain of interest for the student; data cleaning and importing to a tool (if this stage is missed, students can be given a clean data set to use in subsequent stages) Project Stage 2*: analyse the data Project stage 3*: present an explanation and demonstration of some features found in the data Final Exam*: Written exam covering content of lectures and labs, and using skills learned (includes programming questions). Obtaining at least 40% of the available marks from the written exam is a requirement to pass INFO1903. There may be statistically defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes. Tasks that are done in scheduled times cannot be submitted late, except if following the procedures for Special Consideration. For other tasks, a late penalty will be imposed that subtracts 20% of the possible marks per day (or part day) after the due date. |
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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 See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies. |
| 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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| Note on Resources: | Lecture notes, tutorial notes and links to online questions will be provided in the eLearning system |
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 and computation, Unix tools |
| Week 2 | Spreadsheets, Python, regular expressions |
| Week 3 | Databases, SQL, Python |
| Week 4 | Databases, SQL, Python |
| Assessment Due: Asst 1 | |
| Week 5 | Data model, SQL, Python |
| Week 6 | Internet and web technologies, Python |
| Assessment Due: Project Stage 1 | |
| Week 7 | Data pipeline, Internet and web technologies |
| Assessment Due: Mid-Sem Exam | |
| Week 8 | Communication, Data science examples |
| Week 9 | Information visualisation concepts, charting |
| Week 10 | Software engineering, Object-orientation |
| Assessment Due: Asst 2 | |
| Week 11 | Reflection on data management, Java for Python Programmers |
| Week 12 | Guest lecture, Java for Python programmers |
| Assessment Due: Project Stage 2* | |
| Week 13 | Review; Exam structure |
| Assessment Due: Project Stage 3 (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 3) | Yes | 14% |
| Engineering/IT Specialisation (Level 2) | Yes | 36% |
| Maths/Science Methods and Tools (Level 2) | Yes | 5% |
| Information Seeking (Level 2) | Yes | 27% |
| Communication (Level 2) | Yes | 18% |
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.
