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CSC3501 Principles of Data Science and Visualisation

Semester 2, 2022 Springfield On-campus
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Student contribution band : Band 2
Grading basis : Graded
Version produced : 27 June 2022


Examiner: Zhaohui Tang


Pre-requisite: STA3200


Government, private enterprise and science have always been data-driven, what is changing dramatically is the sheer amount of data now generated. Data Science, sometimes also referred to as Big Data, is a rapidly evolving field which studies how to organize, analyse and communicate relevant data through appropriate data visualisations as well as written and oral communications. While data science’s technical foundations arise from Mathematics, Statistics and Computer Science, the area is fundamentally both multi and interdisciplinary. It is most often performed in collaborations across disciplines to bring together the necessary skills and relevant application knowledge. Those with a technical background related to data science need an understanding of the data relevant to the particular problem application area. Those with expertise in the application area must acquire the relevant technical knowledge in order to effectively and accurately make use of data science tools and methodologies.

This course covers the fundamental principles of data science concepts and introduces the student to some of its common tools, methodologies and visualisations. Students will learn how to extract knowledge from data through hands-on experience with common data science programming tools and methodologies. They will create data visualisations to conduct exploratory and confirmatory data analysis. And will gain an appreciation of the breadth of data science applications and their potential value across disciplines.

Course learning outcomes

On successful completion of this course students should be able to:

  1. differentiate between common data science algorithms and identify their appropriate application.
  2. create a reproducible data science project report which includes: all relevant data files, data processing code, visualisations, analyses, reasoning and conclusions.
  3. evaluate a data science problem and apply the appropriate data analyses and problem-solving skills for the successful completion of the data science project.
  4. plan and execute a data science project.


Description Weighting(%)
1. Basic data science algorithms and their applications, such as recommender systems, online advertising, and others depending on selected case studies 20.00
2. Common tools for programming, development and data management 20.00
3. Creating data visualisations for exploratory and confirmatory analysis 20.00
4. Data wrangling 15.00
5. Creating and presenting visualisation models 15.00
6. Mining text from the social web 5.00
7. ethics and data visualization: avoiding misleading graphs 5.00

Text and materials required to be purchased or accessed

Cairo, A 2016, The Truthful Art, New RIders.
VanderPlas, J 2016, Python Data Science Handbook, O'Reilly Media.

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Weighting (%) Course learning outcomes
Assignments Practical Practical 1 No 20 1,2,3
Assignments Practical Practical 2 No 30 1,2,3,4
Examinations Non-invigilated Time limited online examinatn No 50 1,2,3
Date printed 27 June 2022