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Unit of study_

DATA2001: Data Science, Big Data and Data Variety

Semester 1, 2022 [Normal day] - Remote

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'.

Unit details and rules

Unit code DATA2001
Academic unit Computer Science
Credit points 6
DATA1002 OR DATA1902 OR INFO1110 OR INFO1910 OR INFO1903 OR INFO1103 or ENGG1810
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Uwe Roehm,
Lecturer(s) Uwe Roehm,
Tutor(s) Jarrod Jones,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final Examination
Final exam; online, short-release and timed
55% Formal exam period 2 hours
Outcomes assessed: LO1 LO4 LO5 LO6 LO7
Online task Weekly Homework
weekly online quiz in Canvas
10% Multiple weeks ca. 20 min each week
Outcomes assessed: LO2 LO4 LO5 LO6 LO7
Small test SQL Test
SQL online test; mid-semester.
15% Please select a valid week from the list below
Due date: 06 Apr 2022 at 11:00

Closing date: 06 Apr 2022
1 hour
Outcomes assessed: LO1 LO4 LO3
Assignment group assignment Practical Assignment
Practical data integration and data analysis assignment.
20% Week 12 4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4
group assignment = group assignment ?
Type D final exam = Type D final exam ?

Assessment summary

  • Homework: Short weekly homework quizzes in Canvas. They are designed to help you review your learning of each week’s topic.
  • SQL tutorials 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 a mid-semester 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.

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


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.


75 - 84

Awarded when you demonstrate the learning outcomes for the unit at an excellent standard, representing excellence, but substantial less than complete mastery.


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.


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.


0 - 49

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

For more information see

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: -5% of the available 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.

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.

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.

WK Topic Learning activity Learning outcomes
Multiple weeks SQL self-study tutorials via Grok Learning online platform Independent study (16 hr) LO2 LO4
Project Work (practical assignment) - own time Independent study (16 hr) LO1 LO2 LO3 LO4 LO6 LO7
Ongoing revision of weekly material and working on weekly tutorial exercises (homework) Independent study (40 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 01 Introduction and Motivation: What is big data? Challenges for data analytics Lecture and tutorial (4 hr) LO1 LO2 LO3 LO5
Week 02 Data Cleaning and Exploration with Python; Intro Jupyter Notebooks Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 03 Accessing data in relational databases; Introduction to SQL Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 04 Declarative data analysis with SQL Lecture and tutorial (4 hr) LO3 LO4 LO5
Week 05 Scalable data analytics: The role of indexes and data partitioning Lecture and tutorial (4 hr) LO4 LO5 LO6
Week 06 Web Scraping: reading and interpreting data from the web Lecture and tutorial (4 hr) LO1 LO2 LO3 LO7
Week 07 Web APIs and NoSQL: Working with semi-structured data Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 08 Text data processing: feature extraction and analysis Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Week 09 Geo-Spatial Data Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Week 10 Timeseries Data / Health Data Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 11 Image data processing: feature extraction and analysis Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 12 Challenges in analysing Big Data; Data privacy/anonymising data Lecture and tutorial (4 hr) LO5 LO6 LO7
Week 13 Revision Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

Attendance and class requirements

Students are expected to follow the weekly lectures either in-class or using the lecture recordings, and to work through the weekly tutorial material. During the first seven 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 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. 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 have 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

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

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

Revised the weighting of the SQL quizzes.

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 . 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.


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.