FI2BCBD05 Big Data and Advanced Topics
FI2BCBD05 Big Data and Advanced Topics
- Course description
- Course CodeFI2BCBD05
- Level of Study5.1
- Program of StudyData Analyst 2
- Credits5
- Study Plan CoordinatorBertram Haskins, Alec Du Plessis
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.
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
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
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
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) |