Higher Diploma in Science in Data Analytics for Business

Program Overview

The Higher Diploma in Science in Data Analytics for Business is a postgraduate program designed for individuals seeking to enhance their data analytics skills. It provides a comprehensive understanding of programming, machine learning, data analysis, and visualization techniques. The program emphasizes strategic thinking and problem-solving through industry-initiated real-world problems, equipping graduates with the knowledge and skills to make informed business decisions based on data analysis.

Program Outline

Degree Overview:

The Higher Diploma in Science in Data Analytics for Business postgraduate course aims to provide an opportunity for learners with a degree outside the computing arena as well as those currently involved within the IT sphere to refocus and reskill for careers that require Data Analytics knowledge and skills. This programme is specifically designed for individuals with evidenced numerate, technical and analytical ability who aspire to work, or are working, in roles that involve data analysis or the interpretation of data to inform business management and decision-making. They will have the opportunity to continue to develop knowledge, skill and competence to remain competitive and employable in an ever-advancing sector.

Outline:

Programme Content and Modules

Students will undertake learning in the subjects of programming, mathematical, logical and strategic thinking as well as machine learning, data gathering, analysis and visualisation and the subsequent business application of these skills. Industry-initiated real-world problems will be provided by our industry contacts and used as the context for planning and designing assessment solutions, as well as being an aid for problem-solving sessions. In addition to the data analysis and associated technical skills, which will be fostered during the participants studies, transferable skills that will be developed throughout the programme via the varied teaching and assessment methods include: critical analysis, advanced evaluation, self-analysis and personal reflection, problem solving, communication skills, team management and group-work and professionalism. The programme is underpinned by a Strategic Thinking Capstone module which spans all semesters and is assessed by a Problem Based Learning (PBL) project. The module explores current strategic thinking issues companies face today, such as data protection and privacy and the challenges and opportunities of emerging technology.

Strategic Thinking

This capstone module floats across all semesters of the full-time delivery and at least two of three semesters for the part-time delivery. Strategic Thinking concepts are introduced purposefully as the module and programme develops. As this module is the capstone and spans all semesters, the syllabus content usefully synchronises with the principal Problem Based Learning project and associated problem milestones, furthering the relevance of content to practice. The syllabus explores Problem Reduction Identification and Solution Mapping (PRISM), this then builds to project planning and team development specifically within this field.

Statistical Techniques for Data Analysis

This module forms the basis for Numerical methods, particularly those pertaining to statistics and probability which are central to the domain of data analytics. This module will equip the learner with statistical skills that are immediately applicable to basic data analytics tasks as well as serving as a foundation for more sophisticated techniques introduced in later modules.

Data Preparation

This module provides the learner with exposure to extensive exploratory data analysis and proper data management and preparation, which are a crucial first step in any data analysis process. The aim of this module is to provide the learner with an in-depth understanding of the rationale for data exploration and the methods used to explore data. An understanding purpose of feature selection and dimensionality reduction in the context of the curse of dimensionality and the bias-variance trade-off, the importance of the correct encoding of data and the usefulness of feature engineering as a means of representing complex functional relationships to machine learning models.

Machine Learning

This module provides the learner with Machine learning techniques that are an essential component of data analytics. This module builds on and draws from the Statistical techniques for Data Analysis and Data Preparation module to equip the learner with the ability to identify the fundamental nature of data analytical problem and practical experience of the use of commonplace classification and regression approaches.

Data Visualisation Techniques

This module is a key tool in the data analyst’s toolbox, allowing the efficient and effective communication of vast quantities of data, offering rapid insights that would otherwise be difficult or impossible with numerical presentation. This module will provide the learner with the skills needed to present a variety of different types and volumes in data in a manner that provides the maximum insight and understanding to the viewer. Allowing the learner to display directly the results of learning achieved in previous modules.

Machine Learning for Business

This module building on the knowledge acquired in Machine Learning Principles for Big Data, focusing on the available machine learning algorithms widely integrated into commercial machine learning modelling. This module is designed to equip the learner with the skills necessary to tackle a wide range of unsupervised learning problems, such as cluster analysis and text analytics. Both of these techniques are widely used in the analysis of business data as they allow the enterprise to develop a deeper understanding of their customers. The module will also provide the learner with the necessary understanding to be able to perform modelling of temporal data, a type of data that is commonplace in the business domain.

Teaching:

Blended Learning Teaching and Assessment

As the programme is offered in a blended learning format to eligible applicants, students will be required to engage in a combination of on campus and online activities. All students will be introduced to the CCT online learning environment as part of the induction to the programme and will have access to further support as required. Online activities can include live or pre-recorded lectures, independent learning and assessment activities such as research tasks, discussion forums, simulations, quizzes and e-portfolio work along with online group activities such as live classes, group project work, virtual labs and tutorials. Completing the online elements of the programme each week is essential to successfully complete the programme. On campus activities can include small group tutorials, labs, project supervision, problem solving case studies, library research and seminars.