FM1AZDM10 Introduction to Data Mining
FM1AZDM10 Introduction to Data Mining
- Course description
- Course codeFM1AZDM10
- Level of study5.1
- Program of studyApplied Machine Learning
- Credits10
- Course coordinatorLeon Grobbelaar
The course introduces concepts and processes related to principles, methods, implementation techniques, and data mining applications focusing on pattern discovery, cluster analysis, classification and regression. Candidates are provided knowledge to understand the importance of data mining within Machine Learning. The course also provides knowledge and practical skills for analysing data and solving identified issues.
This course is relevant to the program because it is one of the primary foundations of machine learning. It allows students to be able to build intuition about what is happening in data before applying machine learning techniques.
The candidate:
- has knowledge of concepts and processes that are used for data selection in data mining
- has knowledge of methods and tools that are used to gain information from data
- has knowledge of processes and data mining tools that are used to perform data analysis
- has insight into data mining in relation to processed data
- understands the importance of his/her own data mining as a tool for discovering unknowns within data sets
The candidate:
- can apply knowledge to solving practical problems using data mining software
- masters tools and techniques to perform regression, clustering, classification and stream data mining
- masters relevant tools and techniques for implementing data mining channels and evaluating the data
- can update his/her knowledge about data mining and relevant methods and tools to validate data
The candidate:
- understands the ethical principles that apply to data mining
- can carry out work and efficiently extract and reuse data
- can develop, manage and execute data mining projects
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.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Group/Individual | 5 Week(s) |