FM1AZDM10 Introduction to Data Mining
FM1AZDM10 Introduction to Data Mining
- Course description
- Course CodeFM1AZDM10
- Level of Study5.1
- Program of StudyApplied Machine Learning
- Credits10
- Study Plan 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
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 | Group/Individual | 5 Week(s) |