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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, 2021
Attendance mode Normal day
Location Remote
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 (Record+) Type B final exam hurdle task Exam
A mix of multiple-choice, short answer, longer (eg half-page) discussions.
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Skills based evaluation Weekly coding tasks
Produce Python code to meet automated tests (participation mark)
10% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3
Assignment Weekly quizzes
Answer multichoice questions as Canvas quiz (done before lecture)
10% Multiple weeks n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO6 LO7 LO8
Assignment group assignment Project stage 1
Report: describe dataset, metadata, how data was cleaned and ingested.
5% Week 06 n/a
Outcomes assessed: LO1 LO5 LO4 LO3
Skills based evaluation Practice Python coding test A
Extend Python code to pass automated tests
0% Week 07 60 minutes
Outcomes assessed: LO1 LO2
Skills based evaluation Practice Python coding test B
Produce Python code that passes automated tests
0% Week 07 60 minutes
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project stage 2
Report: data summaries and charts, describe computation, evaluation.
10% Week 09 n/a
Outcomes assessed: LO1 LO7 LO3
Skills based evaluation Python coding test A
In a scheduled timeslot, extend Python code to pass automated tests
5% Week 10 60 minutes
Outcomes assessed: LO1 LO2
Skills based evaluation Python coding test B
In a scheduled timeslot, produce Python code that passes automated tests
5% Week 10 60 minutes
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project stage 3
Report with predictive model and evaluation of its success.
5% Week 12 n/a
Outcomes assessed: LO1 LO8 LO3
hurdle task = hurdle task ?
group assignment = group assignment ?
Type B final exam = Type B final exam ?
  • Weekly coding tasks: Tasks where the student must write a Python program to produce precisely described output. The program will be run automatically against test cases. Grade is based on participation. These are started in tutorial, and completed in students’ own time. Late work is not accepted for these assessments. 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, lab, or tutorial content from the previous week. Done in students own time. Late work is not accepted for these assessments. 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 A: Done in student’s own time. There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test, and to trigger remedial learning for any student who does not succeed in the practice. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “No action”.
  • Practice Python coding test B: Done in student’s own time. The student must produce Python code that calculates precisely described output from data in a file. This assessment is graded automatically, by comparing the output produced on several different inputs to what is described. This carries no weight in final grade, but is intended to accustom students to the setting in preparation for the later coding test, and to trigger remedial learning for any student who does not succeed in the practice. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “No action”.
  • Python coding test A: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). There are 5 separate tasks; for each, the student must extend a Python code skeleton so that it calculates precisely described output from data in a file. Each task is graded automatically, by comparing the output produced on several different inputs to what is described. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
  • Python coding test B: Held during a scheduled lecture timeslot, or an alternative scheuled timeslot (for cases of special consideration, or where the lecture timeslot is unsuitable). The student must produce Python code that calculates precisely described output from data in a file. This assessment is graded automatically, by comparing the output produced on several different inputs to what is described. Late work is not accepted for this assessment. When special consideration is approved, the appropriate consideration should be “New or varied assessment”.
  • Project Stage 1: This is the first part of a group project (the students in a group must 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 (including the metadata associated to it), 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”.
  • 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”.
  • 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 consideration is approved, the appropriate consideration should be “extension of time”.
  • Exam: An online exam, with multiple-choice, short-answer, longer (eg half-page) discussions. Covering knowlede, conceptual content, skills, and experiences. Late work is not accepted for this assessment. When special consideration is approved, the appropriate action should be “replacement exam”.

Detailed information for each assessment can be found on Canvas.

Minimum 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.

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.

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.

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 coding tasks, Weekly quizzes, Python coding test A, Python coding test B, final exam, and for all the unweighted formative assessments (practice assessments). Late work is accepted up till 10 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 Introduction and how to learn to program. Data science life-cycle and key data concepts 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: calculate with built-in functions; text processing and data cleaning. Online practical: Spreadsheet data Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Python conditionals and loops. Strings and textfiles Lecture (2 hr) LO1 LO2 LO6
Online tutorial: Summarise a dataset; Advanced text processing 1. Online lab: Spreadsheet calculations Tutorial (2 hr) LO1 LO2 LO3 LO4
Week 04 Python lists. Data aggregation patterns Lecture (2 hr) LO1 LO2 LO3 LO6
Online tutorial: Advanced text processing 2; Bucketing and pivoting numeric data 1. Online lab: group formation Tutorial (2 hr) LO1 LO2 LO3
Week 05 Python dictionaries. Data quality. Lecture (2 hr) LO1 LO2 LO3 LO6
Online tutorial: Bucketing and pivoting numeric data 2. Online lab: Stage 1 work. Tutorial (2 hr) LO1 LO2 LO3 LO4 LO6
Week 06 Data format. Stage 1 discussion; communication principles. Lecture (2 hr) LO5 LO6 LO7
Online tutorial: catchup Python skills; data formats and representation. Online lab: Stage 1 work. Tutorial (2 hr) LO1 LO5 LO6
Week 07 Using modules (csv and pandas). Chart concepts 1. Lecture (2 hr) LO1 LO2 LO3 LO4 LO6 LO7
Online tutorial: use Python matplotlib module for charts. Online lab: Stage 2 work. Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO7
Week 08 Chart concepts 2. Number formats. Lecture (2 hr) LO1 LO2 LO3 LO6 LO7
Online tutorial: chart evaluation and design. Online lab: Stage 2 work. Tutorial (2 hr) LO2 LO3 LO7
Week 09 introduction to machine learning. Stage 2 discussion; Regression and classification predictive models. Lecture (2 hr) LO1 LO2 LO3 LO4 LO8
Online tutorial: revise Python coding skills; use Python csv and pandas modules. Online lab: Stage 2 work. Tutorial (2 hr) LO1 LO2 LO3
Week 10 Scheduled timeslots for Python coding test A and Python coding test B. Lecture (2 hr) LO1 LO2 LO3
Online tutorial: use Python scikit-learn module. Online lab: Stage 3 work. Tutorial (2 hr) LO1 LO2 LO3 LO8
Week 11 Persistent data. Python functions and scope. Lecture (2 hr) LO1 LO2 LO3 LO5
Online tutorial: Data security; version control. Online lab: Stage 3 work. Tutorial (2 hr) LO1 LO2 LO3 LO5 LO8
Week 12 Stage 3 discussion; Python exceptions. More Machine Learning approaches (clustering and recommenders). Lecture (2 hr) LO2 LO3 LO8
Online tutorial: Python exceptions. Online lab: stage 3 work. Tutorial (2 hr) LO2 LO3 LO8
Week 13 Ethics and fairness in data science; Semester review and exam preview. Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Online tutorial: peer-assess practice exam answers. Online lab: peer-assess practice exam answers. Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Weekly Read slides and/or watch prerecorded videos, before lecture timeslot; contribute on discussion boards; do online quiz then correct mistakes; do online Python tasks; either: pre-work to prepare for lab, or else work on project (approx 6 hrs/wk) Independent study (78 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8

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.

Required 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.
  • LO8. understand principles of machine learning and its role in data science, in particular creation, use, and limitations of predictive models for regression and classification tasks, issues of over-fitting and under-fitting, and evaluation of models.

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
Unit has been adjusted following feedback from the initial move online in 2020, to support increased delivery of feedback on assessments and more effective division of work for group assessments.

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