Centre for Professional Development and Lifelong Learning

Postgraduate Modules

Data Mining 1 – Supervised Learning

Data Mining 1 – Supervised Learning

Overview

Data mining is a key method for extracting hidden information and knowledge from large volumes of digital data. This course in data mining will therefore prepare delegates to be competent in the world of “Big Data”. It will help delegates develop a good understanding of the basic concepts, principles and techniques of data mining, and equip them with the skills necessary to process large datasets and solve data science problems using data mining tools and systems. Topics to be covered include: decision trees, support vector machines, neural networks, naïve bayes, ensemble methods, and kernel methods.  

​​Mode of Delivery: Face-to-Face, Online, HyFlex or Blended
 

What will I Learn?

On successful completion of this course delegates will be able to:
  • Justify the application of various supervised learning techniques to solve real-world data science problems
  • Critically evaluate key supervised learning techniques
  • Analyse large datasets using a range of supervised learning techniques
  • Design a decision support system which applies supervised learning methods to real-world data
  • Generate and use effective supervised learning solutions to solve challenging data science problems

Who Should do this Course

Individuals who meet the entry requirements for postgraduate level training, including managers, professionals and data analysts who want to develop knowledge and understanding of the fundamental concepts, principles, and techniques of supervised learning, and who are looking to develop skills in data mining necessary for transforming businesses’ large datasets into powerful and predictive strategic assets.

Important Information

Semester II, 2020/2021 (Jan 2021) postgraduate modules will be delivered ‘face-to-face’, ‘online’, ‘blended’ or ‘hyflex’. See Mode of Delivery definitions below:
 
Face-to-Face
Face-to-face teaching is an instructional method where course content is taught in person, in a physical classroom environment.
 
Online: 
Online teaching is an instructional method where students and instructors connect via technology to review lectures, submit assignments and communicate with one another. No face-to-face learning occurs since lectures, assignments and readings are delivered online.
 
Blended:
Blended teaching (also known as hybrid or mixed-mode) is an instructional method where a portion of the traditional face-to-face instruction is replaced by web-based online instruction. Therefore, classes are delivered via electronic and online media as well as traditional face-to-face teaching.
 
HyFlex:
Hybrid-Flexible (also known as HyFlex teaching) integrates in-class instruction, online synchronous video sessions, or asynchronous content delivery. The instructor will deliver the class in a regular classroom, but students may attend in person, participate in the class through video conferencing, or watch a recording of the class session.

 

  • Applicants must bring all required documentation to the Department of Management Studies, Graduate Section, for their application to be processed.
  • Individuals applying for postgraduate modules, whose native language is not English, must take tests,  to demonstrate English Language proficiency prior to registration, as identified in the Manual of Procedures re: Regulations for Graduate Diplomas and Degrees (Sec. 1, Para. 5).

At a Glance

  • Admissions Term: 2023/2024 Semester II
  • Registration: Open
  • Date: Semester: 1 (September to December) OR Semester: 2 (January to May)
  • Time: Monday, Tuesday, Friday 5pm- 8pm; Saturday 9am- 3pm
  • Duration: 2 weeks (28 hours)
  • Certificate Awarded: Postgraduate Professional Development Certificate of Competence
  • Course Code: BUSA 6003
  • Capacity: 5
  • Cost: BDS $2,385 (US $1,192.50) {with assessment} ; BDS $2,030 (US $1,015) {without assessment}
The following topics will be addressed:
  • Introduction to Supervised Learning
  • Classification and Regression
  • Kernel Methods and Support Vector Machines (SVMs)
  • Decision Trees
  • Statistical Learning Algorithms:
  • Neural Networks
  • Ensemble Methods

For entry into this course, applicants must have at least an undergraduate degree, or five (5) years relevant work experience. Applicants may also be asked to provide an up-to-date Curriculum Vitae

The teaching methods to be used in this course include the interactive lecture, in-class and online discussion (via eLearning), and practical exercises (lab sessions). Much emphasis will be placed on providing students with hands-on experience in applying concepts, principles, and techniques learned through participatory classroom demonstrations and individual/group assignments. The use of various technological media such as PowerPoint and video illustrations of step-by-step procedures will also be incorporated in each session to aid in the teaching of complex concepts.

Terry Harris, PhD – Teaching Fellow in Accounting at Durham University Business School