OCMP5339: Semester 1b, 2024

University home

Library

Current students

Staff intranet

Find an event

Give

Current students

Units OCMP5339 Semester 1b 2024 [Online]

Unit of study_

OCMP5339: Data Engineering

Semester 1b, 2024 [Online] - Online Program

Overview

This unit of study covers the data engineering issues of building robust and scalable data processing pipelines. While data engineers may not be directly performing data analysis, they must have the technical knowledge and skillset to provide data analysts with appropriate data analytics architectures and to provide them with reliable and well-formed data that is ready to be analysed. Topics covered range from data ingestion from various sources including databases, text files and web services, to data cleaning and data transformation approaches, and the system architectures that allow the pipeline to run efficiently and automatically. Special consideration is given to building scalable data analysis solutions using a blend of Big Data processing techniques including data stream processing and distributed data processing platforms such as Apache Spark.

Unit details and rules

Unit code	OCMP5339
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP5329 or COMP4329
Prerequisites ?	COMP5310 or OCMP5310
Corequisites ?	None
Assumed knowledge ?	Proficiency in programming, especially Python, and in database querying with SQL; basic Unix scripting
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Assignment part 1 Practical data integration and data analysis assignment.	15%	Week 03	3 weeks
Outcomes assessed:
Assignment	Assignment part 2 Practical data integration and data analysis assignment.	15%	Week 06	3 weeks
Outcomes assessed:
Supervised exam ?	Final Examination Proctored final exam in Canvas	60%	Week 08 Due date: 07 Jun 2024 at 18:30	2 hours
Outcomes assessed:
Small test	Weekly Review Quizzes Weekly Canvas quiz on concepts taught in this course.	10%	Weekly	ca 30 min per week
Outcomes assessed:

Assessment summary

Details of each assessment tasks appear in Canvas for the course.

The assessment contains three main components:

Knowledge (60%): This is assessed through two primary components: (1) Weekly review quizzes - these are worth 10 marks each and contribute to participation. (2) Final exam (60%) – a formal closed-book online exam covering all materials in lectures, tutorials, and assignments.

Assignment (30%): The assignment will use real world data to build a processing pipeline from ingestion to analytics. It is divided into two parts.

Participation (10%): This is assessed based on students active participation in completing the weekly quizzes about the material covered in the week.

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	Excellent course work
Distinction	75 - 84	Very good course work
Credit	65 - 74	Good course work
Pass	50 - 64	Fair course work
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.

For more information see 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 penalties are applied as per University policy (5% of the maximum mark per day, up to a maximum of 10 days, after which a mark of zero is awarded). There is no late submission possible for the weekly review quizzes.

Academic integrity

The Current Student website provides information on academic integrity 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 integrity breaches seriously.

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Data Engineering; Data Architectures; Unix and Python Refresher	Independent study (6.5 hr)
Week 01	Life Session on Data Engineering and working with CSV data in Unix and Python	Tutorial (1.5 hr)
Week 02	Structured Data and Databases	Independent study (6.5 hr)
Week 02	Life Session on Structured Data and Databases	Tutorial (1.5 hr)
Week 03	Structured Data and the Web	Independent study (6.5 hr)
Week 03	Life Session on Structured Data and the Web	Tutorial (1.5 hr)
Week 04	Working with Spatial-Temporal Data	Independent study (6.5 hr)
Week 04	Life Session on Structured Data and the Web	Tutorial (1.5 hr)
Week 05	Unstructured Data and Data Engineering for ML	Independent study (6.5 hr)
Week 05	Life Session on Unstructured Data and Data Engineering for ML	Tutorial (1.5 hr)
Week 06	Scaling Data Engineering and Security	Independent study (6.5 hr)
Week 06	Life Session on Scaling Data Engineering and Security	Tutorial (1.5 hr)
Week 07	Study, prepare, revise, work on assessments	Independent study (16 hr)

Attendance and class requirements

Attendance at lecture and tutorials is expected.

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 are specified in the weekly material.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. Use appropriate Python libraries to automate data engineering activities on diverse kinds of data
LO2. Use Unix command line to manage and automate data engineering activities
LO3. Ingest, combine and summarise data from a variety of data models
LO4. Demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text
LO5. Understand the main challenges in data engineering: data volume, variety, velocity, veracity, robustness, security
LO6. Demonstrate awareness of ethical and privacy issues when working with data
LO7. Evaluate approaches that store and process data, for correctness, efficiency, and ease-of-use.

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
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered

Additional information

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Computer programming assignments may also be checked by specialist code similarity detection software. These programs work in a similar way to TII in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science 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.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect