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CSC8001 Introduction to Data Science and Visualisation

Semester 2, 2019 On-campus Toowoomba
Short Description: Intro Data Science Visual
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Agric, Comp and Environ Sciences
Student contribution band : Band 2
ASCED code : 020199 - Computer Science not elsewhere
Grading basis : Graded


Examiner: Shelly Grist

Other requisites

While this course does provide an accelerated introduction to fundamental programming knowledge, it is the student’s responsibility to ensure their introductory knowledge of computing is consistent with that found in CSC1401 Foundation Programming. Students without the requisite knowledge may wish to enrol in other courses prior to this course or at least co-enrol to ensure that their introductory knowledge of computing meets this requirement.


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 foundational data science concepts, tools, methodologies and visualisation. 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.


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

  1. differentiate between data science algorithms and interpret their appropriate application across disciplines.
  2. apply data visualisations and written communication, tailored to specific discipline audiences, to report a data science project’s central problem, data analysis, reasoning and conclusions.
  3. identify and apply the appropriate technical processes and problem-solving skills for the successful completion of a 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 10.00

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

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

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.
VanderPlas, J 2016, A Whirlwind Tour of Python, O'Reilly Media.

Student workload expectations

Activity Hours
Assessments 56.00
Examinations 2.00
Private Study 60.00
Workshops 52.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
Assignment 1 100 20 28 Aug 2019
Data Science Project Report 100 30 23 Oct 2019
Exam 100 50 End S2 (see note 1)

  1. This will be a Closed exam. The total working time for the examination is 2 hours. The examination date will be available via UConnect when the official examination timetable has been released.

Important assessment information

  1. Attendance requirements:
    There are no attendance requirements for this course. However, it is the students' responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    To complete each of the assessment items satisfactorily, students must obtain at least 50% of the marks available for each assessment item.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4).

  4. Requirements for student to be awarded a passing grade in the course:
    To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.

    Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.

    To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure (point 4.4.2).

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    This is a Closed examination. Candidates are allowed to bring only writing and drawing instruments into a closed examination.

  7. Examination period when Deferred/Supplementary examinations will be held:
    Any Deferred or Supplementary examinations for this course will be held during the next examination period.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at