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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

DATA1002: Informatics: Data and Computation

This unit covers computation and data handling, integrating sophisticated use of existing productivity software, e.g. spreadsheets, with the development of custom software using the general-purpose Python language. It will focus on skills directly applicable to data-driven decision-making. Students will see examples from many domains, and be able to write code to automate the common processes of data science, such as data ingestion, format conversion, cleaning, summarization, creation and application of a predictive model.

Details

Academic unit Computer Science
Unit code DATA1002
Unit name Informatics: Data and Computation
Session, year
? 
Semester 2, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
INFO1903 OR DATA1902
Prerequisites
? 
None
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Alan Fekete, alan.fekete@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Exam
A mix of short answer and longer (eg half-page) discussions.
50% Formal exam period 3 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Skills based evaluation hurdle task Oral exam barrier
answer questions on core content, in recorded online conversation
0% Formal exam period 10 minutes
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO7
Online task Weekly quizzes
Answer multichoice questions as Canvas quiz (done before lecture)
10% Multiple weeks n/a
Outcomes assessed: LO2 LO7 LO6 LO5 LO4 LO3
Skills based evaluation Weekly Python tasks
Produce Python code that passes automated tests, submitted via Groklearning
10% Multiple weeks n/a
Outcomes assessed: LO1 LO3 LO2
Online task Practice Python coding test
Done in scheduled online tutorial session, using Groklearning platform
0% Week 06 50 minutes
Outcomes assessed: LO1 LO2
Assignment group assignment Draft of Project stage 1
Preliminary draft
0% Week 07 n/a
Outcomes assessed: LO1 LO5 LO4 LO3
Assignment group assignment Project stage 1
Report describes dataset, source, and how data was cleaned and ingested.
5% Week 08 n/a
Outcomes assessed: LO1 LO5 LO4 LO3
Online task Python coding test
Done in scheduled online tutorial session, using Groklearning platform
10% Week 09 50 minutes
Outcomes assessed: LO1 LO2
Assignment group assignment Draft of project stage 2
Preliminary draft
0% Week 10 n/a
Outcomes assessed: LO1 LO7 LO3
Assignment group assignment Project stage 2
Report that shows data summaries and charts, describes computation used.
10% Week 11 n/a
Outcomes assessed: LO1 LO7 LO3
Assignment group assignment Draft of project stage 3
Preliminary draft
0% Week 11 n/a
Outcomes assessed: LO1 LO4 LO3
Assignment Practice final exam
Practice answering questions like in final exam
0% Week 11 3 hrs
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment group assignment Project stage 3
Report with predictive model and evaluation of its success.
5% Week 12 n/a
Outcomes assessed: LO1 LO4 LO3
hurdle task = hurdle task ?
group assignment = group assignment ?
Type D final exam = Type D final exam ?
  • Weekly Python tasks: The material in the GrokLearning platform includes tasks where the student must write a Python program to produce precisely described output. The program will be graded automatically. These are started in tutorial, and completed in students own time. When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
  • Weekly quizzes: Each quiz consists of multiple-choice questions related to the lecture content from the previous week. Done in students own time. When special consideration is approved for a task, the appropriate consideration should be “mark adjustment” based on estimating a grade using the average from other tasks or the final exam.
  • Practice Python coding test: Held during scheduled tutorial sessions. Each student will be required to produce Python code that calculates precisely described output from data in a file. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test. When special consideration is approved, the appropriate consideration should be “No action”.
  • Python coding test: Held during scheduled tutorial sessions. Each student will be required to produce Python code that calculates precisely described output from data in a file. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
  • Draft project stage 1. This carries no weight in final grade, but allows feedback from tutors or peers. When special consideration is approved, the appropriate consideration should be “No action”.
  • Project Stage 1: This is the first part of a group project (the students in a group should all be attending the same scheduled lab session). This stage involves finding data from a domain of interest for the students, data cleaning and importing to a tool, and doing a very simple analysis from some of the data. A report is required that describes the dataset, how it was obtained, and how it was processed by the tool. If this stage is missed or badly done, the group can be given a clean data set, for a domain chosen by the instructor, to use in the rest of the project. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special consideration is approved, the appropriate consideration should be “extension of time”.
  • Draft project stage 2. This carries no weight in final grade, but allows feedback from tutors or peers. When special consideration is approved, the appropriate consideration should be “No action”.
  • Project Stage 2: The group will use computational tools to explore the data, and report on both what was done and what was found (using appropriate summaries and charts). It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special consideration is approved, the appropriate consideration should be “extension of time”.
  • Draft project stage 3. This carries no weight in final grade, but allows feedback from tutors or peers. When special consideration is approved, the appropriate consideration should be “No action”.
  • Project Stage 3: The group will use computational tools to produce a predictive model for some aspect of the data, and evaluate this model; deliverable is a report on both what was done and what was found. It is crucial that each group manages its internal working effectively, and use mechanisms to detect problems and report them to the coordinator early. When special considewration is approved, the appropriate consideration should be “extension of time”.
  • Practice final exam: In 3 hrs in students own time, produce and upload a document with answers to a set of questions covering conceptual content, skills, and experiences. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later final exam. When special consideration is approved, the appropriate consideration should be “no action”.
  • Exam: A written exam, covering conceptual content, skills, and experiences. When special consideration is approved, the appropriate action should be “replacement exam”
  • Oral exam barrier: a short online conversation with tutor or lecturer, for student to demonstrate knowledge of the main factual content, in a situation where identity can be checked (eg by sighting a student card). This carries no weight in final grade, but satisfactory performance is a barrier required to Pass the unit. When special consideration is approved, the appropriate consideration should be “replacement assessment”.

Detailed information for each assessment can be found on Canvas.

Minimum requirement: To Pass thius unit, a student must achieve an overall mark of at least 50, and also, they must have satisfactory performance in the hurdle task “oral exam barrier”.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late work is not accepted for the following assessments: Weekly Python tasks, Weekly quizzes, oral exam barrier, final exam, and for all the unweighted formative assessments (practice assessments, draft project stages). Late work is accepted up till 10m days late, subject to the standard penalties (subtract 5% of maximum possible mark, per day late) for Project stage 1, Project stage 2, Project stage 3. However, note that work submitted late may not receive feedback before the next stage is due.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Data science life-cycle and key data concepts; How to learn to program Lecture (2 hr) LO3 LO4 LO5
Online Tutorial: Use Python as calculator. Online Lab: Examine a dataset Tutorial (2 hr) LO1 LO2 LO3 LO5
Week 02 Python concepts; Spreadsheet concepts Lecture (2 hr) LO1 LO2 LO4 LO5
Online tutorial: Use function, conditional, text and strings. Online practical: Excel Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Conditionals, for-loops, strings Lecture (2 hr) LO1 LO2 LO6
Online tutorial: aggregate in loop, list, nested loop, index. Online lab: Excel Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 04 Lists; more loops Lecture (2 hr) LO1 LO2 LO3 LO6
Online tutorial: sequence manipulation, dictionary. Online lab: group formation Tutorial (2 hr) LO1 LO2 LO3 LO6
Week 05 Python dictionaries; Communication and reports Lecture (2 hr) LO1 LO2 LO3 LO6 LO7
Online tutorial: tuple, nested dictionary, revision. Online lab: evaluating reports Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Week 06 Data formats; data quality; data persistence Lecture (2 hr) LO3 LO5 LO6
Online tutorial: practice coding test. Online lab: data formats Tutorial (2 hr) LO1 LO5 LO6
Week 07 Using modules, pandas, number representations; simulation and optimization Lecture (2 hr) LO1 LO2 LO3 LO4 LO6
Online tutorial: use csv and pandas modules. Online lab: feedback on project stage 1 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 08 charts; using matplotlib Lecture (2 hr) LO1 LO2 LO7
Online tutorial: write functions, matplotlib. Online lab: finish project stage 1. Tutorial (2 hr) LO2 LO3 LO7
Week 09 introduction to machine learning; Python functions; scope Lecture (2 hr) LO1 LO2 LO3
Online tutorial: coding test. Online lab: feedback on project stage 2. Tutorial (2 hr) LO1 LO2 LO3 LO7
Week 10 regression, classifier; over-fitting and under-fitting Lecture (2 hr) LO2 LO3
Online tutorial: use scikit-learn module. Online lab: finish project stage 2. Tutorial (2 hr) LO1 LO2 LO3 LO7
Week 11 clustering; recommenders; Python exceptions Lecture (2 hr) LO1 LO2 LO3
Online tutorial: Python exceptions. Online lab: feedback on project stage 3. Tutorial (2 hr) LO1 LO2 LO3 LO7
Week 12 data management policy; versions; workflow; fairness; exam preview Lecture (2 hr) LO3 LO5
Online tutorial: notebooks, version control. Online lab: peer-assess practice exam answers. Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Weekly Watch prerecorded lecture videos; contribute on discussion boards; do online quiz then correct mistakes; do online Python tasks on Groklearning platform; either: pre-work to prepare for lab, or else work on project (approx 6 hrs/wk) Independent study (72 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

Laboratories: attendance at lab sessions is crucial, as this is where groups are formed, work together, and get feedback on the project stages.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Prescribed readings

These books are optional extra reading; they can be accessed through the Library eReserve, available on Canvas.

  • J. Grus, Data Science from Scratch 2nd ed. O`Reilly, 2019. isbn 1492041130.
  • J. Guttag, Introduction to Computation and Programming Using Python: With Application to Understanding Data 2nd ed. MIT Press, 2016. isbn 0262529629.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. automate a computational process, when given a clear account of the algorithm to be applied (to be done by writing Python programs with core techniques of procedural programming)
  • LO2. demonstrate knowledge of Python syntax and semantics, to trace and understand idiomatic code typical of data science activities, including features such as user-defined functions, exception-raising, and handling
  • LO3. understand automation of the computational process needed for examples of the various activity in the data science pipeline: data ingestion and cleaning, data format conversion, data summarization, visual and tabular presentation of the results from summarization, creation of a predictive model of a given form, application of a predictive model to new data, evaluation of a predictive model (and also, automation of a pipeline that scripts use of existing tools for these activities)
  • LO4. understand both spreadsheets, and programs in Python, for automatically performing computational processes of data science, and awareness of the similarities and differences between tools
  • LO5. understand main issues for data management in connection with data science activities, including value of data, importance of metadata, and issues when sharing data across time and users
  • LO6. understand how data sets are represented in computer files, in particular, the many-to-many relationship between the physical representation and the logical representation; advantages and disadvantages of different representations
  • LO7. understand principles of charting and information presentation, and ability to produce good charts using both Python libraries and spreadsheets; also capability to evaluate charts for effectiveness in communication.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
This unit has changed in many details since 2019, mainly to deal with online-only study. In addition, project work has been divided more carefully, to help students see the differences between important skills which all need to be achieved.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.