Post Graduate Diploma in Management (Major in Business Analytics)

SISH Institute’s Post Graduate Diploma Programmes are for individuals who want to acquire the specialised knowledge and qualifications required to explore and enter into a different industry, or for existing professionals who aspire to career progression.

Students will learn from a network of established industry practitioners; this will enable them to have an extra edge in securing fruitful employment and build their careers upon graduation. Students will gain a solid understanding of the business and management aspects of the industry from the course major selected.

Course Objectives

This course is restructured as a “bridging programme” for a subsequent higher-level course. Upon completion of this programme, students will proceed to take up the course for an MBA degree of whichever university would accept them.

Hence, students, on top of meeting the requisite admission criteria, must have explicit intention to progress to the MBA course before they are formally admitted into this course.

Learning Outcomes

Upon completion of the course, learners will be able to:

  • Demonstrate and contrast relevant operational principles in various types of organisations
  • Research, organise and critically analyse information for the further application or dissemination
  • Identify real-world problems drawn from the business and management aspects of the industry from the course major selected by: ranking central issues, generating alternative solutions, offering persuasive reasons and evidence
  • Devise and attain appropriate goals through organising, motivating, coordinating and cooperating with peers

Awarding Body

  • SISH Institute

Diploma Awarded

  • Post Graduate Diploma in Management (Major in Business Analytics)

Entry Requirements

Minimum Age21
Language RequirementAttainment of IELTS 6.0 or equivalent
Academic Requirement

Normal Entry:

Obtained a Bachelor Degree in any field or equivalent

Alternative Entry:

Applicants with the minimum age of 30, who do not possess the above qualifications but have a minimum of 8 years of working experience, may be considered for entry into this course. Evidence of previous employment will need to be provided as well as a reference from the current/most recent employer (on letter-headed paper)

Work ExperienceNot Applicable

Mode of Delivery

  • Classroom-based facilitation and blended with the mode of deliveries that may consist of group discussions, case analysis, demonstration workshops, role-plays, individual and group tutorials.

Assessment Methods

  • Blended assessment components, including formative activities, projects and written examinations.

Graduation Requirements

  • Attain a minimum pass with ‘C’ grade in all the required modules that include 4 core modules and 2 elective modules.

Progression Pathway

  • This is not a pathway programme

Course Duration

  • Full-time: 6 months
  • Part-time: 9 months

Next available intake(s)

  • The intake for this program is every two (2) months (January, March, May, July, September, November)

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. 


Application Fee (Non-Refundable)
Course Fees

  • Click here for Miscellaneous Fees
  • Click here for Refund Policy

Register for Course

Submit the following documents and email to


  • All prices stated above are inclusive of the prevailing Goods & Services Tax (GST)
  • Local Applicant: Singapore Citizens and Permanent Residents
  • International Applicant: International students including international students transferring from other local institutes

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