Teaching Responsibility
LJMU Schools involved in Delivery:
Computer Science and Mathematics
Learning Methods
Lecture
Practical
Module Offerings
7505BDSA-JAN-PAR
Aims
The course covers the concepts and techniques of data mining. It provides students with a detailed knowledge about descriptive and predictive data mining methods that can be used to extract hidden patterns from data such as visualization, classification, clustering, association, estimation, etc. Topics of handling missing data and dealing with outliers are also covered by the course.
Learning Outcomes
1.
Apply DM methodology (e.g. CRISP) to extract hidden patterns from large databases.
2.
Implement appropriate DM technique for real life cases.
3.
Interpret and critically evaluate results obtained from the application of DM techniques to take proper decisions accordingly.
4.
Conduct DM research (individually and in groups).
5.
Evaluate privacy, legal, and ethical issues related to DM.
Module Content
Outline Syllabus:Introduction to DM
Data Preparation
Data Transformations
Knowledge Representation
Algorithms
Evaluation
Implementations
Beyond Applications
Additional Information:The module contributes to the master’s aim to equip the student with the required abilities and skills to perform data science on real-world applications.