Postgraduate Diploma in Management (Business Analytics)

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The module is designed to provide the learners with the opportunity to be familiar with all the components of the marketing mix. They will be exposed to discussions that could develop skills to craft marketing strategies and prepare them to complete marketing plans for companies in different sectors, environments and situations, in accordance with their global policies and strategies

The module is designed to enable potential managers to review the  creation of financial statements (accounting cycle) and then learn about  users and providers of management accounting information. Learners also  learn internal accounting terms and procedures; use of accounting data  in planning, control and performance evaluation; assumptions and  limitations of conventional accounting measurements.

 The module is designed to equip learners with the abilities and skills to adapt and manage an increasingly diverse human resource, and in a competitive business environment. The areas include diversity management; job analysis and job design; human resource planning; and recruiting, screening and selection of candidates; interviewing candidates; orientation, training and development; compensation, incentives and benefits; evaluating and appraising employee performance; and organizational culture and cross-cultural issues.

The module is designed to present the nature and purpose of strategic  management from local and global perspectives, strategic management  models, strategy formulation, external environmental analysis, internal  environmental analysis and evaluation, competing in the global  marketplace, strategy implementation, strategy monitoring through  balanced scorecards, issues related to the organization’s functional  areas in relation to the corporate business strategy, strategy review,  evaluation and control, as well as corporate social responsibilities of  companies that implement an integrative corporate strategy.

This module is designed to discover the analytics way of investigating  huge amount of data for the benefit of life standards and acquisition of  competitive knowledge and skills relevant in today’s business  environment. The availability of big data makes it possible for  businesses to explore human and consumer behaviour through patterns and  descriptions. Interpretation of data has never been so in-demand until  these days when available tools, techniques, and technologies make it  possible to dive deep into analytical processes that are only possible  with human manipulation using digital processes and platforms.

This module is designed to introduce the learners to machine learning  and introduction to Python as the coding program. It does not delve on  the methods’ statistics but rather on the methods in machine learning.  It differentiates between machine learning and descriptive statistics.  Other topics to be covered in this module are clustering task of data,  cluster assessment, data dimensionality, and basics of Python, scikit  learn, other learning algorithms, predictive models, scikit learn  predictive modelling methods, data generalizability, basics of  ensembles, practical limitations of predictive models, supervised  (classification), unsupervised (clustering) techniques, and Python  coding for analysis.

This module is designed to introduce the types of relational databases  and how to use SQL (Structured Query Language) to manipulate such  databases. Data has become the language of online business as well as  brick and mortar ventures. Power comes from techniques on how to manage  the voluminous amount of data from customer preferences, behaviour,  and habits, to data of medical patients, not only their personal  particulars but medical histories, diagnosis, and cures. One of the ways  to extract data from such deluge of database sources is through the SQL  (pronounced also as “sequel”) language.