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INFO1903: Informatics (Advanced) (2017 - 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:
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
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
3 Independent Study 6.00 13
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:
# Name Group Weight Due Week Outcomes
1 Asst 1 No 10.00 Week 4 (Thursday) 1, 8, 9,
2 Project Stage 1 No 5.00 Week 6 (Thursday) 1, 2, 6, 8, 9, 10,
3 Mid-Sem Exam No 5.00 Week 7 4, 5, 6, 8, 9,
4 Asst 2 No 10.00 Week 10 (Thursday) 1, 2, 4, 5, 8,
5 Project Stage 2* No 10.00 Week 12 (Thursday) 3, 4, 5, 10,
6 Project Stage 3 (Oral Presentation and Handout)* No 10.00 Week 13 3, 10,
7 Final Exam* No 50.00 Exam Period 1, 2, 4, 5, 6, 7, 8, 9, 10,
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.
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.
Minimum Pass Requirement It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
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.
  • Learning Python
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 Year(s) Offered
Bachelor of Information Technology (Computer Science) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Bachelor of Computer Science and Technology 2015, 2016, 2017
Bachelor of Computer Science and Technology (Advanced) 2015, 2016, 2017
Bachelor of Computer Science and Technology (Computer Science) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Bachelor of Computer Science and Technology (Computer Science)(Advanced) 2014 and earlier 2013, 2014
Bachelor of Computer Science and Technology (Information Systems) 2014 and earlier 2013, 2014
Bachelor of Computer Science and Technology (Information Systems)(Advanced) 2014 and earlier 2013, 2014
Bachelor of Computer Science & Tech. Mid-Year 2016, 2017
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
Electrical (Computer) / Science 2015
Electrical (Power) / Science 2015
Electrical (Telecommunications) / Science 2015
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
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Information Technology (Computer Science) / Science 2012, 2013, 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2011, 2012, 2013, 2014
Information Technology (Information Systems) / Science 2012, 2013, 2014
Flexible First Year 2016+ / Science 2016, 2017, 2018, 2019, 2020
Flexible First Year (Stream A) / Science 2015, 2012, 2013, 2014
Flexible First Year (Stream B) / Science 2012, 2013, 2014
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Information Technology (Computer Science)/Arts 2012
Information Technology (Computer Science) / Commerce 2012
Information Technology (Computer Science) / Medical Science 2012
Information Technology (Computer Science) / Law 2012
Information Technology (Information Systems)/Arts 2012
Information Technology (Information Systems) / Commerce 2012, 2013, 2014
Information Technology (Information Systems) / Medical Science 2012, 2013, 2014
Information Technology (Information Systems) / Law 2012, 2013, 2014
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017

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.