COMP5339: Semester 2, 2023

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Units COMP5339 Semester 2 2023 [Normal evening]

Unit of study_

COMP5339: Data Engineering

Semester 2, 2023 [Normal evening] - Camperdown/Darlington, Sydney

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	COMP5339
Academic unit	Computer Science
Credit points	6
Prohibitions ?	OCMP5339
Prerequisites ?	COMP5310
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	Yes

Teaching staff

Coordinator	Bob Kummerfeld, bob.kummerfeld@sydney.edu.au
Lecturer(s)	Bob Kummerfeld, bob.kummerfeld@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final Examination Final exam; in-person written exam.	60%	Formal exam period	2 hours
Outcomes assessed:
Online task	Weekly on-line quiz A short in-class quiz in weeks 2 to 13..	10%	Multiple weeks	10 mins
Outcomes assessed:
Small test	Skills test Short test of coding skills.	5%	Week 04	20 minutes
Outcomes assessed:
Assignment	Assignment part 1 Practical data integration and data analysis assignment.	10%	Week 08 Due date: 24 Sep 2023 at 23:59	4 weeks
Outcomes assessed:
Assignment	Assignment part 2 Practical data integration and data analysis assignment.	15%	Week 12 Due date: 29 Oct 2023 at 23:59	4 weeks
Outcomes assessed:

Assessment summary

Weekly Quizzes: These are short quizzes to test the material covered in the previous week. The better mark principle will be used for the total marks on the weekly quizzes, so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz. The better mark principle means that the total weekly quiz marks counts if and only if it is better than or equal to 10% of your exam mark.
If your total weekly quiz mark is less than 10% of your exam mark, then 10% of your exam mark will be used instead. This allows you to improve in the exam.

Skills test: this is a short test of coding ability.

Assignment: the assignment will use real world data to build a processing pipeline from ingestion to analytics. It is divided into two parts.

Final exam: The final exam is a written exam covering all materials in lectures, tutorials, and assignments.

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. 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 final 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 sydney.edu.au/students/guide-to-grades .

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and Motivation: What is data engineering? The data pipeline.	Lecture and tutorial (3 hr)
Week 02	Data cleaning and transformation on the command line. Data engineering and the cloud.	Lecture and tutorial (3 hr)
Week 03	Flat files vs databases	Lecture and tutorial (3 hr)
Week 04	Databases as source and sink. Data Warehousing.	Lecture and tutorial (3 hr)
Week 05	Data from the web: Python scraping, APIs	Lecture and tutorial (3 hr)
Week 06	Semi-structured data: storing and querying. NoSQL databases	Lecture and tutorial (3 hr)
Week 07	Spatial data engineering	Lecture and tutorial (3 hr)
Week 08	Temporal Data	Lecture and tutorial (3 hr)
Week 09	Unstructured data: image and text	Lecture and tutorial (3 hr)
Week 10	Unstructured data: feature extraction	Lecture and tutorial (3 hr)
Week 11	Scalable Data Engineering	Lecture and tutorial (3 hr)
Week 12	Stream Data Processing	Lecture and tutorial (3 hr)
Week 13	Security and Privacy	Lecture and tutorial (3 hr)

Attendance and class requirements

Students are expected to follow the weekly lectures either in-class or using the lecture recordings, and to attend the weekly tutorials.

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

N/A

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.

This is the first time this unit has been 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

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

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