Outline

Overview

This course focuses on methods and techniques to efficiently explore and analyse large data collections. Where are hot spots of pedestrian accidents across a city? What are the most popular travel locations according to user postings on a travel website? The ability to combine and analyse data from various sources and from databases is essential for informed decision making in both research and industry. Students will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects, such as relational, semi-structured, time series, geospatial, image, text. As well as reinforcing their programming skills through experience with relevant Python libraries, this course will also introduce students to the concept of declarative data processing with SQL, and to analyse data in relational databases. Students will be given data sets from, eg. , social media, transport, health and social sciences, and be taught basic explorative data analysis and mining techniques in the context of small use cases. The course will further give students an understanding of the challenges involved with analysing large data volumes, such as the idea to partition and distribute data and computation among multiple computers for processing of 'Big Data'. This unit is an alternative to DATA2001, providing coverage of some additional, more sophisticated topics, suited for students with high academic achievement.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	75% or above from (DATA1002 OR DATA1902 OR INFO1110 OR INFO1903 OR INFO1103)
Corequisites ?	None
Prohibitions ?	DATA2001
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Uwe Roehm, uwe.roehm@sydney.edu.au
Lecturer(s)	Alan Fekete, alan.fekete@sydney.edu.au
Tutor(s)	Sidath Randeni Kadupitige, sidath.randenikadupitige@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final Examination Final examination; online, short-release and timed	60%	Formal exam period	2 hours
Outcomes assessed:
Online task	Weekly Homework weekly online quiz in Canvas	10%	Multiple weeks	ca. 20 min each week
Outcomes assessed:
Online task	SQL Test SQL online test; mid-semester	10%	Please select a valid week from the list below Due date: 21 Apr 2021 at 11:00 Closing date: 21 Apr 2021	1 hour
Outcomes assessed:
Assignment	Practical Assignment Practical data integration and data analysis assignment.	20%	Week 12	ca 4 weeks
Outcomes assessed:
= group assignment ? = Type D final exam ?

Assessment summary

Homework: Short weekly homework online quizzes in Canvas. They are designed to help you review each week’s topic.
SQL tutoral and SQL Test: Students work through weekly online tutorials introducing increasingly sophisticated usage of SQL. The SQL tutorials provide simple feedback and allow multiple attempts, and example solutions are available after the submission deadline has passed. Solutions are provided for each week, and the topics are assessed in an mid-semster SQL test.
Practical Assignment: Students work in teams on a larger data integration and data analysis task, where some supplied datasets have to be combined with additional data researched by students. The final submission consists of the source code artifacts developed by the teams, plus a short report of their findings, and a group demo during the labs of Week 12.
Final Examination: Understanding of all of this unit’s material is reviewed in an final, online examination.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, representing complete or close to complete mastery of the material.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at an excellent standard, representing excellence, but substantial less than complete mastery.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, representing creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, representing at least routine knowledge over a spectrum of topics and important ideas and concepts taught in this unit of study.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

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 submission penalty for the practical assignment: -20% of the awarded marks per day late; minimum 0% after 5 days. Homework quizzes can be done anytime during the week they are released up-to their deadline.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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
Multiple weeks	SQL self-study tutorials via Grok Learning online platform	Independent study (16 hr)
Multiple weeks	Project Work (practical assignment) - own time	Independent study (16 hr)
Ongoing	revision of weekly material and working on weekly tutorial exercises (homework)	Independent study (40 hr)
Week 01	Introduction and Motivation: What is big data? Challenges for data analytics	Lecture and tutorial (4 hr)
Week 01	Advanced topic: Data Analysis with Unix	Seminar (1 hr)
Week 02	Data Analysis with Python / Jupyter Notebooks	Lecture and tutorial (4 hr)
Week 02	Advanced topic: Unix and Jupyter magics	Seminar (1 hr)
Week 03	Accessing data in databases; introduction to SQL	Lecture and tutorial (4 hr)
Week 03	Advanced topic: SQLite / ipython-SQL	Seminar (1 hr)
Week 04	Declarative Data Analysis with SQL	Lecture and tutorial (4 hr)
Week 04	Advanced topic: Descriptive Statistics with SQL	Seminar (1 hr)
Week 05	Scalable Data Analytics: the role of indexes and data partitioning	Lecture and tutorial (4 hr)
Week 05	Advanced topic: Trend Analysis in SQL; Window Queries	Seminar (1 hr)
Week 06	Web Scraping: Reading and interpreting data from the web	Lecture and tutorial (4 hr)
Week 06	Advanced topic: Web Scraping with Unix Shell	Seminar (1 hr)
Week 07	Web APIs and NoSQL: Working with semi-structured data	Lecture and tutorial (4 hr)
Week 07	Advanced topic: Web API Debugging; Scrapy	Seminar (1 hr)
Week 08	Geo-Spatial Data	Lecture and tutorial (4 hr)
Week 08	Advanced topic: Text Classification with sciKit-learn and SpaCy	Seminar (1 hr)
Week 09	Advanced topic: How to get computation close to data? A look at MADlib	Seminar (1 hr)
Week 09	Text Data Processing: feature extraction and analysis	Lecture and tutorial (4 hr)
Week 10	Analysing Time Series Data	Lecture and tutorial (4 hr)
Week 10	Advanced topic: Introduction to Apache Spark	Seminar (1 hr)
Week 11	Image Data Processing: feature extraction and analysis	Lecture and tutorial (4 hr)
Week 11	Advanced topic: pySpark and Sparkmagic	Seminar (1 hr)
Week 12	Challenges in analysing Big Data: the What and Why of Hadoop; 2. data privacy/anonymising data	Lecture and tutorial (4 hr)
Week 12	Advanced topic: Big Data Machine Learning	Seminar (1 hr)
Week 13	Revision	Lecture and tutorial (4 hr)

Attendance and class requirements

Students are expected to follow the weekly lectures and the additional advanced seminar either in-class or using the lecture recordings, and to work through the weekly tutorial material. During the first eight weeks of the unit, there is also an online SQL tutorial available which students are expected to work through on their own time in order to prepare for the mid-semester SQL quiz.

The practical assignment is group work where all team members are expected to actively participate and to divide the work fairly among the team members. The individual mark awarded for the group assignment is conditional on a team member being able to explain any part of the group submission to the tutor or the lecturer if asked. In particular, groups will have to demo their submissions in the tutorials of Week 12, and based on this demo, the group’s assignment mark will be scaled for each team member based on the individual level of contribution. Further details of this participation scaling will be defined on the assignment handout.

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.

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 science activities on diverse kinds of data
LO2. ingest, combine and summarise data from a variety of data models
LO3. demonstrate experience with handling datasets of diverse kinds of data, including relational, semi-structured, time series, geo-location, image, text, including experience to combine data of different types
LO4. understand and produce declarative queries to extract appropriate information from data sets, including competence in use of SQL
LO5. understand the main challenges analysing 'big data': data volume, variety, velocity, veracity
LO6. understand the impact of data volume on data processing, and awareness of approaches to address this such as indexing, compression, data partitioning, and distributed processing frameworks (Hadoop)
LO7. demonstrate awareness of privacy issues when working with data
LO8. know and work with several sophisticated topics related to data scale and diversity.

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.

On student request from last year, introduced weekly review quizzes.

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. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/) . 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

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

Current students

DATA2901: Big Data and Data Diversity (Advanced)

Semester 1, 2021 [Normal day] - Remote

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

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