Centre for Professional Development and Lifelong Learning

Postgraduate Modules

Data Preparation and Visualization

Data Preparation and Visualization

Overview

Data gathering is often a tedious and arduous job. Once gathered, it typically includes mistakes, omissions, and inconsistencies that can significantly distort the results of data analysis. As a result, data preparation is an inevitable and vital step that needs to be carried out before analysing any large dataset. The goal of this course therefore is to introduce delegates to different tools and techniques designed for collecting data and preparing them for further analysis. The course will cover: obtaining data from the web, APIs, and databases in various formats, detecting errors in large datasets, and the basics of data cleaning.  

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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:
  • Identify various sources of data;
  • Critically evaluate the role that visualization plays in the processing and analysis of data;
  • Use effectively the tools and techniques of data preparation to clean, summarise, and manipulate data;
  • Critically evaluate the key design principles and techniques for visualizing data;
  • Determine which visualization techniques are appropriate given the particular requirement imposed by the data;
  • Use effectively several data visualization tools to design and create data visualisations appropriate to the specific audience type, task, and data source.

Who Should do this Course

Individuals who meet the entry requirements for postgraduate level training working including those who work with data, or within the analytics field who want to develop the practical skills necessary to clean, summarise, and manipulate data and to represent large datasets in a visual format.

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: 2025/2026 Semester II
  • Registration: Open
  • Date: January to May 2026
  • Time: TBA
  • Duration: 12 weeks (36 hours)
  • Certificate Awarded: Postgraduate Professional Development Certificate of Competence
  • Course Code: BUSA 6002
  • Capacity: 5
  • Cost: BDS $2,385 (USD $1,192.50) {with assessment}; BDS $2,030 (USD $1,015) {without assessment}

The following topics will be addressed:

  • Course Overview and Introduction to Data preparation and visualisation
  • Obtaining Data
  • Data Preparation
  • Data Summarisation
  • Data Visualisation
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 lab sessions. Much emphasis will be placed on providing delegates 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 critical and complex concepts and methods.

Terry Scantlebury