Diploma in Business Intelligence & Analytics

The NYP Professional Competency Model for the Diploma in Business Intelligence & Analytics consists of:

33 Competency Units – Within each transdisciplinary Competency Unit, skills and knowledge from different disciplines are integrated and taught holistically. This enables the students to fulfil specific work-tasks upon completion. Completing the Competency Units will also equip our students with nine Course Competencies

Five Competency Canvases – where skills and knowledge learnt are assessed for competency, together with a Work-Integration-Unit (WIU) which is a workplace contextualised project. You have to complete four to graduate.
One Final Year Project (FYP).

One Internship Placement (ITP).

10 General Studies Modules (GSMs) – these modules give you exposure to other areas of interest and allow you to go beyond your chosen area of specialisation,

Students enrolled in Diploma in Business Intelligence & Analytics under the Professional Competency Model will have to complete 33 Competency Units (CUs). Within each transdisciplinary CU, skills and knowledge from different disciplines are integrated and taught holistically, enabling students to fulfil specific worktasks upon completion.

Completing the CUs will also equip our students with the nine Course Competencies (CCs) below.

Data Visualisation & Journalism
Analytics & Computational Modelling
Applied Artificial Intelligence
Data Administration & Management
Analytics with Programming

Data Strategy & Design
Emerging Technology & Applications
Business Needs Analysis & Strategy
Data Security & Governance

Students will also need to complete Work-Integration-Unit (WIU) which can be in the form of an internship or project that mirror work situations where students can demonstrate their skills and competencies.

CUs / WIUs in Year 1

Applied Cryptography (30 hours)

In today's digital world, business data is an important asset that needs to be safeguarded from intruders. Learners will develop competencies in applying cryptographic algorithms and techniques to secure the business data. Learners will be competent to facilitate group discussions in recommending appropriate cybersecurity tools and applying data privacy principles in accordance with the organisation’s security requirements.

Business Needs Analysis (30 hours)

Business needs analysis is the process of discovering, analysing, defining and documenting the requirements that are related to specific business needs. Through this CU, learners will develop competencies in gathering business requirements from key stakeholders with different cultural backgrounds and diverse needs using effective communication and interpersonal skills. Learners will be competent in working collaboratively to document user stories leveraging online collaboration tools. Learners will also be competent in analysing user requirements and establishing the user acceptance criteria to meet the business needs and deliver service excellence.

Data Modelling (30 hours)

Data modelling is the process to define the conceptual data model based on business needs. Through this CU, learners will develop the competencies by analysing raw business data based on its characteristics to design a conceptual data model in given business scenarios. Learners will also be competent in implementing a data schema in a database management system using conceptual data modelling techniques

Data Storage Administration (30 hours)

With tremendous data growth, data storage platform must be able to scale with growth and continue to provide consistent levels of performance. Through this CU, learners will develop competencies in designing, creating, managing and administering data storage. Learners will be competent in performing data administrative tasks based on email correspondences with key stakeholders. Learners will also be competent to perform data retrieval and data manipulation tasks by using Extract-Transform-Load (ETL) tools and techniques.

Data Structures & Algorithms (60 hours)

With the increased complexity and data volume of enterprise-level applications, appropriate data structures and efficient algorithms are essential in business application development. Through this CU, learners will develop competencies in manipulating static and dynamic data structures. Learners will be competent in using searching and sorting algorithms for effective problem-solving. Learners will also be competent in demonstrating the sharing of knowledge, ideas and experience to support peer learning

Data Visualisation (30 hours)

Data visualisation provides a powerful way to communicate data-driven insights and discover patterns to detect anomalies. Through this CU, learners will develop competencies in building interactive and effective visualisations that address international and cultural differences, diverse needs and expectations of the key stakeholders. Learners will also be competent to perform data exploratory analysis to discover patterns and trends

Decision Analysis (60 hours)

Decision analysis is a systematic approach in making the best decision over a range of possible options in a given business situation. Through this CU, learners will develop competencies in constructing mathematical models to represent business problems. Learners will also be competent in making strategic business decisions to solve real-world problems with given constraints.

Network Administration (60 hours)

Network technology provides end-to-end solutions to connect devices, allowing seamless sharing of applications and data. Through this CU, learners will develop the competencies in implementing Internet Protocol (IP) addressing scheme and subnetting based on the network requirements of Wide Area Network (WAN) or Local Area Network (LAN). Learners will be competent to work collaboratively and effectively in teams to design and configure networks for small and medium enterprises. Learners will also be competent to diagnose and troubleshoot network connectivity problems and network equipments using common network tools.

Operating Systems Administration (60 hours)

Operating systems are an essential part of any computer system. It provides the basic computing resources for application programs and acts as an intermediary between the computer user and the computer hardware. Through this CU, learners will develop competencies in administering the core components of operating systems such as resource management, user management, permissions, command and control, domains and groups and file systems to achieve optimised system performance. Learners will be competent to work collaboratively in teams to automate administrative tasks in the operating system using a scripting language.

Programming (60 hours)

Programming is an essential part of learning computer science by formulating real-world problems into computational algorithms. Through this CU, learners will develop competencies in designing and constructing computer programs using programming constructs, data types and algorithms to solve computational problems. Learners will also be competent to perform software validation using appropriate software testing and debugging techniques.

Statistical Research Methods (60 hours)

In today's business world, decision-making has become more data-driven. Statistical methods are widely used in the industry to analyse raw business data, extract information from data, summarise data, make inferences and draw conclusions from data. Through this CU, learners will develop competencies to draw insights from data by assessing or predicting an event’s probability of occurrence with associated confidence requirements for decision-making. Learners will also be competent in performing business research by forming hypotheses, using sampling techniques, establishing research validity and reliability, writing the scientific research paper as well as performing relevant statistical inference techniques to conclude the research data.

UX Design (60 hours)

User Experience (UX) provides a positive experience through any form of human-computer interaction. Through this CU, learners will develop competencies in designing and creating intuitive user interfaces using web technologies for enhanced user experience. Learners will also demonstrate competencies in undertaking a technical lead role to drive the team in building intuitive and responsive web pages for optimal viewing on all devices.

Visual Analytics Project (WIU) (30 hours)

Visual analytics is an approach that uses data visualisation tools and techniques to visualise data insights, trends, patterns and relationships in business data. Through this WIU, learners will demonstrate their competencies in developing a visual analytics project in teams by gathering, identifying, analysing business requirements from key stakeholders with diverse needs to establish business problems. These business problems will be discussed and analysed using statistical modelling techniques to support strategic decision-making. Learners will also demonstrate their competencies in preparing the data using ETL techniques and developing visualisations to communicate business data insights effectively to the key stakeholders with diverse cultural backgrounds.

CUs/ WIUs in Year 2

Big Data Modelling & Management (60 hours)

NoSQL fulfils the volume, variety and velocity requirements of large distributed data. Through this CU, learners will develop competencies to design NoSQL data storage to cope with the processing demands of Big Data. Learners will also be competent in applying appropriate data access patterns in accordance with the different Big Data scenarios.

Customer Experience Analysis (30 hours)

A customer experience analysis is an in-depth study of evaluating how a customer interacts with products or services. Through this CU, learners will develop competencies in collecting and analysing data on customer experience and behaviour using research frameworks and historical data that comply with data and privacy ethics. Learners will also be competent in applying analytical techniques that help in segmenting, profiling and ranking customers from international and diverse cultural groups on various metrics to achieve expected service excellence.

Data Integration & Clustering (30 hours)

The imputation of data into Big Data platform is a prerequisite for further processing of Big Data. Through this CU, learners will develop competencies in performing data migration tasks using appropriate tools and techniques between local and distributed file systems. Learners will also be competent in developing scripts to manipulate larger datasets in the Big Data warehousing platforms to reduce potential human errors.

Data Journalism (30 hours)

Data storytelling are interactive, visually compelling and provide deep business insights. Through this CU, learners will develop competencies in designing and developing interactive dashboards using visual mapping techniques to visualise underlying data patterns in business data. Learners will also be competent in associating related dashboards into data stories to communicate effectively with stakeholders of different cultural background to meet their diverse needs and expectations.

Data Privacy & Protection (30 hours)

Data privacy has become a key consideration in data collection and sharing of data. Through this CU, learners will develop competencies to apply data governance and privacy principles that drive data confidentiality, integrity, availability, authentication and non-repudiation. Learners will also be competent with the data ethics, privacy acts and laws, regulations and policies that are relevant to data protection.

Data Wrangling (30 hours)

Data wrangling is the process of transforming raw data into an appropriate and usable format for data analysis. Through this CU, learners will develop competencies in collecting data from multiple data sources using appropriate data collection tools and techniques in accordance with data ethics related to cybersecurity and privacy. Learners will also be competent in using appropriate tools and techniques for transformation, reshaping and aggregation of data from multiple sources.

Emerging Technology Synthesis (30 hours)

Emerging technologies offer opportunities to create new business and revenues that drive business innovation. Through this CU, learners will develop lifelong learning competence as they conduct continual market research to discover emerging technologies, trends and industry developments based on research findings. Learners will be competent to work collaboratively in a team and propose leveraging emerging technologies to address business needs using cost‐benefit analysis and relevant evaluation methods. Learners will also be competent in collaborative proposal writing using online collaboration tools.

Natural Language Processing (30 hours)

Natural Language Processing (NLP) is a subfield in artificial intelligence, concerned with the interactions between computers and human languages. Through this CU, learners will develop competencies in natural language processing to analyse large amount of human language data with a global perspective for international and diverse cultural groups. Learners will also be competent in performing information retrieval, language translation tasks to build NLP systems using text mining techniques.

Programming for Data Science (60 hours)

Data processing is integral to the use of Machine Learning as the quality of data directly affects the ability of the model to learn. Through this CU, learners will develop competencies in performing Extract-Transform-Load (ETL) tasks from the various data source using programming to solve business problems. Learners will be competent in discovering underlying data relationships among data variables through data exploratory analysis. Learners will also be competent to perform statistical analyses and predict future trends from data with the creation of Machine Learning models.

Supervised Learning (60 hours)

Supervised learning is a type of Machine Learning algorithm that models relationships and dependencies between the target prediction output and input features. Through this CU, learners will develop competencies in applying relevant predictive modelling techniques to predict the desired business outcomes and meet the service expectation of the stakeholders. Learners will also be able to evaluate the accuracy and effectiveness of different predictive models using relevant metrics as well as to support peer learning.

Topic Modelling & Sentiment Analysis (60 hours)

Topic modelling is used for document clustering, organizing large blocks of textual data and information retrieval from structured/unstructured data. Through this CU, learners will develop competencies in analysing large amount of textual data from diverse cultural groups to discover key topics and its association using text mining techniques. Learners will also be competent in undertaking a technical lead role in text analytics tasks to identify and extract subjective information from textual conversations.

Unsupervised Learning (30 hours)

Unsupervised learning is a type of Machine Learning algorithms that helps to discover data patterns without pre-defined business variables. Through this CU, learners will develop competencies in dimensionality reduction, clustering and association techniques to discover underlying patterns in data to solve business problems. Learners will also be competent to work collaboratively in a team to discover inherent relationships and associations in business data.

Web Application Development (60 hours)

Web application development is the process of developing a dynamic web application that involves both client-side and server-side programming skillsets. Through this CU, learners will develop competencies in creating interactive and rich media web pages using web technologies. Learners will also be competent to integrate data storage in the web application using server-side programming. Learners will demonstrate competencies in undertaking a technical lead role to drive team discussions to generate new ideas in building web applications and collaborate using source version control tools.

Predictive Analytics Project (WIU) (30 hours)

Predictive analytics generates future insights with a significant degree of precision based on historical data. Through this WIU, learners will demonstrate their competencies in developing a predictive analytics project in teams by applying data privacy and ethical principles in the collection, use and disposal of data. With the data collected, learners will demonstrate their competencies to impute data format, transform and reshape the business data before applying relevant predictive modelling techniques to predict the desired business outcomes. Learners will then develop data stories for an effective narrative and visual representation of their predictive analytics project.

Text & Social Analytics Project (WIU) (30 hours)

With a large amount of data generated by social media platforms, text analytics provides an effective way to conduct market research on product branding and reputation. Through this WIU, learners will demonstrate their competencies in developing a text & social analytics project in teams by analysing textual data such as customer feedback, social media conversations and textual documents to understand customer experiences, preferences and needs with consideration of diverse cultural differences. Learners will be competent to perform data processing operations in NoSQL database and common text analytics tasks such as entity extraction, text categorisation, clustering, association and trend analysis in the textual data that comply with data and privacy ethics. Learners will also be competent in undertaking a technical lead role to drive team discussions in developing data stories for an effective narrative and visual representation of their text & social analytics project.

CUs / WIUs in Year 3

Advanced Data Visualisation (30 hours)

Data visualisation libraries give the ability to create customised visualisations. Through this CU, learners will develop competencies in designing dynamic and interactive data visualisations using scripting languages. Learners will also be competent to illustrate how Application Programming Interface (API) libraries can be creatively integrated into their data analytics applications in a team setting.

AI Services in Analytics (30 hours)

Artificial intelligence (AI) is revolutionising business by automating repetitive learning and discovery through data. Through this CU, learners will develop competencies to select suitable cloud-based AI services to solve business problems. Learners will also be competent to drive smart inventions and build intelligent applications rapidly.

Applied Deep Learning (30 hours)

Deep learning is an AI approach that mimics the workings of the human brain in processing data for decision-making. Through this CU, learners will develop lifelong learning competence as they conduct continual market research to discover trends and industry developments using deep learning frameworks and based on research findings. Learners will also be competent to work collaboratively in teams to select and use suitable deep learning networks to solve business problems in different domains.

Applied Machine Learning (60 hours)

Machine learning is a rapidly growing field in computer science to discover underlying patterns in a massive amount of data using statistical modelling. Through this CU, learners will develop competencies in applying Machine Learning algorithms on Big Data using supervised and unsupervised learning techniques to forecast business outcomes. Learners will also be competent in training Machine Learning models to support decision-making and solve business problems.

Data Platforms Management (30 hours)

Big data platforms offer solutions that meet the increased needs of businesses for greater scale and analytics with data. Through this CU, learners will develop competencies in managing data cluster resources on Big Data platforms. Learners will be competent in making operational decisions by managing data nodes in Big Data clusters to achieve optimal performance.

Data Processing on Big Data (30 hours)

Big Data can be defined as high volume, velocity and variety of data that require high-performance processing. Through this CU, learners will develop competencies in managing parallel processing resources on large data set in Big Data platforms. Learners will also be competent in creating computer programs to facilitate parallel querying, aggregation and processing of data in a distributed Big Data platform.

Machine Learning Operations (30 hours)

Machine Learning Operations (MLOps) provide a scalable and governed means to deploy and manage Machine Learning models / Deep Learning networks in production environments. Through this CU, learners will develop competencies in deploying models to MLOps cloud platforms. Learners will also be competent to work collaboratively in teams to use validated models deployed on the MLOps cloud platforms.

Marketing Strategy (30 hours)

Marketing analytics gives an insightful view of how businesses can be improved to achieve organisational targets through marketing efforts. Through this CU, learners will develop competence in lifelong learning as they conduct continual market research to discover new and critical customer segments, market gaps and competitors based on research findings. Learners will also be competent to work collaboratively in teams to generate fresh perspectives and ideas as they develop their marketing strategy and pitch the proposed marketing plan to key stakeholders of international and diverse cultural groups.

Final Year Project (480 hours)

Through the Final Year Project, learners will synthesise the knowledge and skills they have developed from the course to deliver IT solutions for real-life problems. Projects may range from industry-commissioned, R&D to poly-wide and learner-initiated projects. Besides enhancing their Software Development skills, learners will develop Problem Solving and Lifelong Learning Skills as they work independently, leveraging new technologies and software tools to achieve the various milestones set by their Project Supervisors. Working within diverse group settings, learners will also deepen their repertoire of 21st century skills to prepare them for entry into the workforce.

Internship Programme (480 / 960 hours)

Through this Internship Programme, learners will be assigned to real-life workplace environments to receive On-the-Job Training (OJT) where they can apply the knowledge and skills acquired in the course. The learners will be guided by Industry Internship Mentors (IIMs) and School Internship Mentors (SIMs) in achieving the learning outcomes according to the OJT blueprint. This will allow learners to develop competencies and experiences at the workplace. In addition, they will acquire important work values and ethics which include being responsible and positive, as well as taking initiative and exercising integrity. Through this OJT, learners will be industry and future-ready.

Select one out of two WIUs

Applied AI Project (WIU) (30 hours)

AI applications are created to solve immensely difficult issues in various industries. Through this WIU, learners will demonstrate their competencies in developing an applied AI project in teams by selecting and using suitable deep learning networks to solve business problems in different domains. Learners will be competent in deploying models as microservices built from different programming languages to MLOps cloud platforms to facilitate application integration. Learners will also be competent to undertake a technical lead role to drive team discussions in designing and developing intelligent machine reasoning applications using appropriate cloud-based AI services in the Applied AI project.

Big Data Management (WIU) (30 hours)

The combination of Big Data and Machine Learning will drive business innovation across industries by analysing massive amounts of data and deriving predictive insights. Through this WIU, learners will demonstrate their competencies in developing a Big Data management project in teams by handling Big Data and managing it in a Big Data platform. Learners will be competent to work collaboratively in a team to develop and deploy Machine Learning models using open-source libraries to fit the data to a probability distribution. Learners will also be competent in undertaking a technical lead role to drive team discussions in designing real-time visualisations using API libraries and integrate them into the Big data management project.

Five Competency Canvases – where skills and knowledge learnt are assessed for competency, together with a Work-Integration-Unit (WIU) which is a workplace contextualised project. You are required to choose, and complete four Competency Canvases to graduate.

The following list reflects the Competency Units and Work-Integration Units mapped under each Competency Canvas.

Certificate in Visual Analytics

Business Needs Analysis
Data Storage Administration
Data Visualisation
Decision Analysis
Visual Analytics Project (WIU)

Certificate in Predictive Analytics in Data Science

Data Journalism
Data Privacy & Policy
Data Wrangling
Unsupervised Learning
Predictive Analytics Project (WIU)

Certificate in Text and Social Analytics

Big Data Modelling & Management
Customer Experience Analysis
Data Journalism
Topic Modelling & Sentiment Analysis
Text & Social Analytics Project WIU

CHOOSE EITHER

Certificate in Applied Artificial Intelligence

AI Services in Analytics
Applied Deep Learning
Data Processing on Big Data
Machine Learning Operations
Applied AI Project (WIU)

Certificate in Big Data Management

Advanced Data Visualisation
Applied Machine Learning
Data Processing on Big Data
Programming for Data Science
Data Platforms Management
Big Data Management Project (WIU)

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