FI2BCCT05 Critical Data Thinking
FI2BCCT05 Critical Data Thinking
- Course description
- Course CodeFI2BCCT05
- Level of Study5.1
- Program of StudyData Analyst 1
- Credits5
- Study Plan CoordinatorBertram Haskins, Alec Du Plessis
This course teaches candidates high-level data theory concepts and processes. Candidates are introduced to practical solutions towards building legal compliance databases which comply with GDPR protocol. Candidates are exposed to critical thinking concepts such as technology-based decision-making, structured versus unstructured data plans, business intelligence systems, data sustainability theory, data lake theory, and advanced data modelling techniques. A refresher in communication skills to make topics less technical for a wider audience is also discussed.
This course is relevant since it teaches students top-down data strategies they can apply to future projects. Candidates will learn how to enforce GDPR and legal compliance at a practical level and learn how to identify systems not meeting standard requirements. Additionally, candidates will engage in concepts used during project planning phases in the industry. Understanding these concepts is only possible after all technical skills have been learned and applied within the data analysis course.
The candidate:
- has knowledge of technology-based decision-making techniques that are used to assist in selecting long-lasting system usability
- can assess own work in relation to GDPR protocols at every stage of data analysis
- is familiar with the distinctive nature of critical data thinking and the importance of communicating technical information to a non-technical audience
- has insight into own opportunities for development in the critical data thinking and GDPR protocols that apply to the field of data analysis
The candidate:
- can explain implementation steps for full GDPR and legal compliance to all systems involved in a data analysis lifecycle
- can explain vocational choices of data lakes, data sustainability, and advanced models to existing systems to improve the quality of their output
- can reflect on own communication skills towards the non-technical audience and adjust them under supervision
- can find and refer to work methods that enrich analysis systems towards the newest data control techniques
- can find and refer to information and vocational material to develop data analysis techniques that cater for business intelligence systems
The candidate:
- can contribute to developing effective work methods to improve analysis accuracy and data safety using advanced modelling techniques
- can exchange points of view with their peers and participate in discussions about critical data thinking and data safety
Digital Learning Resources
The learning management system (LMS) is the primary learning platform where students access most of their course materials. The content is presented in various formats, such as text, images, models, videos or podcasts. Each course follows a progression plan, designed to lead students through weekly modules at their own pace. Exercises and assignments (individual or in groups) are embedded throughout the courses to support continuous practice and assessment of the learning outcomes.
Campus Resources
In addition to the digital learning resources, campus students participate in physical learning activities led by teachers as part of the overall delivery.
Guidance
Guidance and feedback from teachers support students' learning journeys, and may be provided synchronously or asynchronously, individually or in groups, via text, video or in-person feedback.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Individual | 1 Week(s) |