FI2BCBD05 Big Data and Advanced Topics

FI2BCBD05 Big Data and Advanced Topics

  • Course description
    • Course code
      FI2BCBD05
    • Level of study
      5.1
    • Program of study
      Data Analyst 2
    • Credits
      5
    • Course coordinator
      Bertram Haskins, Alec Du Plessis
Teaching term(s)
2025 Spring
Authors
Alec Du Plessis
About the Course

This course teaches candidates the integration of data science and data engineering into aspects of the data analysis field. Candidates will be introduced to big data theory at a practical level, as well as how to use existing machine learning assets and model clusters. This course does not teach candidates how to create new machine learning systems. Instead, it will teach them how to discover the required services to integrate pre-trained models and artificial intelligence systems into their datasets. Additionally, advanced implementations of query-based tools such as Power Bi and SQL databases will be explored.

This course is relevant because most modern cloud-based data analysis lifecycles utilise big data in their systems. Candidates must be able to identify appropriate pre-trained models and how to apply said models to their respective data projects. Candidates must be able to use these tools alongside previous theory-learned tools to assemble a complete process but must first understand critical data theory. Candidates can quickly integrate cutting-edge machine-learned data sets and artificial intelligence components into any required data projects.

Course Learning Outcomes
Learning outcomes - Knowledge

The candidate:

  • has knowledge of Big Data theory, existing machine learning assets and model clusters that are used to integrate pre-trained models and artificial intelligence systems into datasets
  • is familiar with the traditions and distinctive nature of data science and data engineering into aspects of the data analysis field
  • has insight into own opportunities for further development within industry relevant tools such as Power BI and SQL databases
Learning outcomes - Skills

The candidate:

  • can explain vocational choices of required services to integrate pre-trained models and artificial inelegance into data sets
  • can reflect over own vocational practice with existing machine learning assets and model clusters and adjust it under supervision
  • can find and reflect to information and vocational material about pre-trained models and how to apply these models to their respective data projects
General Competence

The candidate:

  • can plan and carry out integration of machine-learned data sets and artificial intelligence components to relevant data projects alone or as a part of a group and in accordance with ethical requirements and principles
  • can exchange points of view with their peers and participate in discussions about integration of data science and data engineering into aspects of the data analysis field
Teaching and Learning

In this course, the following teaching and learning methods can be applied, but are not limited to:

  • Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
  • Group work: Collaborative activities where students work together to solve problems or complete tasks.
  • Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
  • Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
  • Online lessons: Digital content delivered via an online learning platform.
  • Guidance: Individualized advice and direction from instructors to support students in their learning journey.
  • Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
  • Self-study: Independent study where students engage with course material on their own without any teacher support.
Reading list

Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessment
Course Assignment
Pass / Fail
Group/Individual
1 Week(s)
Approved by
x.x
Accreditation
x.x