| Semester 2, 2022 Toowoomba On-campus | |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Overview
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鈥檚 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.
Course learning outcomes
On successful completion of this course students will be able to:
- differentiate between data science algorithms and interpret their appropriate application across disciplines.
- apply data visualisations and written communication, tailored to specific discipline audiences, to report a data science project鈥檚 central problem, data analysis, reasoning and conclusions.
- identify and apply the appropriate technical processes and problem-solving skills for the successful completion of a data science project.
- plan and execute a data science project.
Topics
| 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
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
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Practical 1 | No | 20 | 1,2,3 |
| Practical 2 | No | 30 | 1,2,3,4 |
| Time limited online examinatn | No | 50 | 1,2,3 |