Diploma in Data Science (N81)

Why DS?

  • Get groomed for the future economy with a solid foundation in data analytics and machine learning
  • Gain hands-on experience in a one-year Industry Kickstart Programme
  • Learn from the best in the industry by tapping on the expertise from our own Data School 
  • Earn professional certifications from industry leaders such as Microsoft Azure, Tableau, SAP Analytics Cloud, Google Analytics, Salesforce, Alteryx and Singapore Computer Society

About DS

With each click, like and share, data is being created every second around the world. There’s never been a greater demand for data science professionals to help businesses derive customer insights, drive traffic and boost profits. If you have a passion for data analytics and machine learning, and aspire to join the big data industry, the Diploma in Data Science (DS) could be the course for you!

Tap on the expertise of the school that founded Data School, alongside leading industry partners such as OCBC, Indorse and SGInnovate. With an industry-focused curriculum, you’ll be at the forefront of data analytics and machine learning technologies.

To ensure that you’re well-equipped for your digital future, you’ll build core skills in programming, databases and analytics. Besides understanding data science fundamentals and how they affect the competitiveness of organisations, you’ll also learn key statistical concepts and data visualisation techniques for analyses and presentations. These competencies will stand you in good stead when you pursue a career as a data analyst, junior data scientist, business intelligence manager or associate data engineer/business analyst.

With exposure to reputable enterprise platforms including Microsoft Azure, Salesforce, Tableau, SAP, Alteryx and TIBCO, you’ll develop strong programming and analytical skills that are vital in managing the AI project life cycle. 

Thanks to our strong industry links, you’ll get to go on a one-year Industry Kickstart Programme to enhance your workplace readiness. Integrating skills and knowledge, you’ll embark on industry-driven data science projects that let you explore emerging trends and apply machine learning techniques in end-to-end data science workflows.

Keen to dive even further into AI? Join ICT’s Artificial Intelligence Special Interest Group where you’ll have hands-on experience in machine learning and AI applications, and industry networking through events, competitions and talks.

You can also look forward to internships, workshops, field trips as well as industry mentorship as part of your learning journey in DS. Plus, you’ll have opportunities to put your skills to the test in national level competitions like the PolyFintech100 Hackathon.

To get an added edge in the workplace, you can pursue additional professional certifications, such as Microsoft Certified: Data Analyst Associate, Salesforce ADX 201 Certification, Alteryx Designer and Tableau Desktop Specialist.

Highlights

Industry-driven Data Training

Widen your career options with the Data School, a collaboration between ICT and industry partners such as OCBC, Indorse and SGInnovate. You can look forward to taking skills-focused short courses, which are co-designed and co-delivered with the industry to provide career-relevant training that will prepare you for future roles in data analysis and data science.

OCBCxNAP

Visit https://www.cet.np.edu.sg/signature_programme/data-school/ to find out more.

Further Studies

You can receive advanced standing when you apply for related degree programmes at universities both locally and abroad. These include:

  • National University of Singapore
  • Nanyang Technological University
  • Singapore Institute of Technology
  • Singapore Management University
  • Singapore University of Technology and Design

You can also look forward to pursuing a specialist diploma in an analytics-related field and other advanced diploma courses at local polytechnics.

evelyn-peh

Evelyn Peh
Financial Informatics* graduate, Class of 2020

Pursuing a degree in Information Systems, with a specialisation in FinTech and Business Analytics, at SMU

*Renamed the Diploma in Data Science 

alyssa-nah

Alyssa Nah
Financial Informatics* graduate, Class of 2019

Pursuing a Bachelor of Computing in Information Systems at NUS

*Renamed the Diploma in Data Science

Careers

With the amount of data growing exponentially, there is a greater need for professionals who can read and analyse it. In November 2019, the Smart Nation and Digital Government Office launched the National Artificial Intelligence (AI) Strategy as a key step in Singapore’s Smart Nation journey. As Singapore embarks on national projects and initiatives to encourage businesses to leverage AI, there will be an increased demand for professionals who possess skills in areas such as data extraction and wrangling, as well as machine learning and deep learning techniques. 

You can look forward to pursuing careers in these job roles upon graduation: 

  • Associate Business Analyst 
  • Associate Data Engineer 
  • Data Analyst 
  • Junior Data Scientist

Entry Requirements

AGGREGATE TYPE ELR2B2-C

To be eligible for consideration, candidates must have the following GCE ‘O’ Level examination (or equivalent) results.

Subject'O' Level Grade
English Language1-7
Mathematics (Elementary/Additional)1-6
Any two other subjects1-6

You must also have sat for one subject listed in the 2nd group of relevant subjects for the ELR2B2-C Aggregate Type listed here (  33KB). This subject will be used for ELR2B2-C aggregate computation.

For students with other qualifications, please refer to the NP website for the entry requirements and admissions exercise period.

Candidates with severe vision deficiency should not apply for the course.

What You Will Learn

Computing Mathematics (4 Credit Units)

This module introduces the basic concepts of relations and functions, matrices, statistical methods and relevant applications. The main emphasis is to develop students’ ability in solving quantitative problems in computing mathematics, probability and statistic.

Cyber Security Fundamentals (2 Credit Units)

This module provides an overview of the various domains of cyber security. It helps to develop an understanding of the importance of cyber security in today’s digital world. It aims to provide an appreciation of cyber security from an end-to-end perspective. It covers fundamental security concepts, tools and techniques in domains such as data, end-user, software, system, network, physical, organization, and digital forensics. It also helps to develop knowledge and skills in identifying common cyber threats and vulnerabilities, and to apply techniques to tackle these issues.

Data Science Fundamentals (2 Credit Units)

This module provides an overview of Data Science, its importance in the world of data and how it affects the competitiveness of organizations. Learners will learn about the different areas within Data Science and the core pillars essential to practise in the area. Students will also be introduced to Design Thinking. Indicative topics include Introduction to Data Science, Big Data and Analytical Design Thinking.

Design Principles (2 Credit Units)

This module introduces students to basic elements and principles of design. Students will practice visual communication and self-branding through aesthetic use of line, shape, form, color, texture, typography, scale, contrast, rhythm and balance. Students will be trained in the usage of digital design tools and application of modern industrial practices to communicate the concepts, designs and solutions.

Fundamentals for IT Professionals 1 (2 Credit Units)

This module provides a broad introduction to the field of ICT by exploring the roles, professional practice, ethical expectations and career development paths of IT professionals. Through a guided inculcation of interpersonal and teamwork skills with strong team bonding spirit, the module aims to deepen students’ commitment to the sector that the course prepares them for. In addition, students will be required to begin charting their career path in the ICT industry by considering crucial aspects such as personal preferences and aptitude, job roles and responsibilities, skills needed and further education.

Programming 1 (5 Credit Units)

This module introduces the fundamentals of programming and how to develop programs using appropriate problem-solving techniques in a modular style. In this practice-oriented module, students are taught how to apply problem-solving skills using a top-down structured programming methodology and given ample practice in translating solutions into computer programs, then test and debug the programs. Topics include data types, variables, expressions, statements, selection structures, loops, simple computation and algorithms, and the use of libraries. Students will also practise the use of pseudocodes, best practices of programming, debugging techniques with the help of tools, development of test cases, and suitable program documentation. In addition, they will study various areas where application software plays a prominent part in helping organisations solve problems. Student will be given ample opportunity for independent and self-directed learning.

English Language Express* (Credit Units - NA)

English Language Express aims to give you a better grounding in the English Language and to strengthen the written and oral communications skills that you will need in your academic and professional careers. You will be engaged in writing, reading, listening and speaking activities that will develop your ability to speak and write grammatically, coherently and clearly. You will also hone your reading and listening comprehension skills.

Health & Wellness^ (1 Credit Unit)

Innovation Made Possible^ (3 Credit Units)

Databases (4 Credit Units)

Today’s business organisations depend on information systems in virtually all aspects of their businesses. Corporate databases are set up to hold the voluminous business transactions generated by these information systems. This module introduces students to the underlying concepts of database systems and how to model and design database systems that reflect business requirements. Students will be taught how to analyse data needs, model the relationships amongst the data entities, apply the normalisation process to relations and create the physical database. Skills taught include data modelling technique, transformation of data model to relations, normalisation technique and SQL (Structured Query Language).

Data Discovery & Visualisation (4 Credit Units)

This module discusses the principles and techniques for creating effective visualisations based on graphic design and perceptual psychology. Using widely adopted tools and analytical programming, learners will apply these principles and techniques to create rich visualisations for analysis and presentation. Indicative topics include Principles of Visualization, Dashboard Design Techniques and Designing for an Audience.

Mathematics for Data Science (4 Credit Units)

In this module, students will first be exposed to statistical concepts, including hypothesis testing, probability distribution and more. Students will be able to perform univariate, multivariate and correlation analysis in order to identify inherent patterns and derive key insights from business data. Indicative topics include Normal Distribution, Sampling and Sampling Distributions and Correlation Analysis.

Programming 2 (4 Credit Units)

This module builds upon the knowledge and skills acquired in Programming I. It aims to provide opportunities for the students to develop medium-scale applications based on the Object-Oriented (OO) approach. A suitable object- oriented high-level programming language will be used by students to apply in their problem-solving skills. The main concepts of OO and the implementation of applications using the OO approach will be taught in this module.

The module may cover the concepts of Abstract Data Types (ADTs) and the implementation of some selected ADTs using the OO approach. Suitable sorting and search algorithms and the use of Application Protocol Interface (API) will be introduced when required. Other key topics include the introduction of system design concepts such as the class diagram. Software robustness and correctness, and good programming practices will be emphasised throughout the module. Independent and self-directed learning will also be encouraged.

Communication Essentials^ (3 Credit Units)

Intelligent Enterprise Systems (4 Credit Units)

The use of intelligent enterprise systems has become a necessity in multi-national companies as well as small and medium enterprises. This module introduces students to the different components that build up an intelligent enterprise system. Students will be able to appreciate the complexity of business processes, how IT can help organisations to be more competitive and gain basic management skills that are required to manage business processes in an organisation.

Data Exploration & Analysis (4 Credit Units)

In this module, students will experience the process of exploratory data analysis, normalization of data and data distribution analysis, which will be crucial for subsequent understanding of machine learning concepts and models. Students will explore data using a combination of statistical and visualisation techniques. Indicative topics include Data Warehousing, Data Dimensional Modelling and Data Mining.

Data Wrangling (4 Credit Units)

This module focuses on the use programming libraries and shell scripting techniques to clean and prepare data for analysis and modelling purposes. Emphasis will be placed on the Extraction, Transformation, and Loading (ETL) of data sets. Indicative topics include Storage and Database Connections, Manipulation of Datasets and Web Scraping.

Fundamentals for IT Professionals 2 (2 Credit Units)

This module gives a course-based experience in which students can engage with the local community and industry. This includes participation in community service events or in Service-Learning projects that leverages on students’ discipline knowledge and skills to meet identified needs. Through iterative and guided reflection on the service experience, students gain a broader appreciation of their discipline and an enhanced sense of personal voice, empathy and civic responsibility. Industry talks and seminars are organised to keep students up to-date with emerging trends and develop their interpersonal, team and networking skills with the community and industry.

Elective Module 1# (4 Credit Units)

Electives offered by the Diploma in Data Science:

  • Accounting
  • Advanced Databases
  • Applied Analytics
  • Banking Applications and Processes
  • Cloud Architecture & Technologies
  • Customer Decision-Making & Negotiation Skills
  • Customer Experience Management
  • Deep Learning
  • Enterprise Business Processes
  • Enterprise Resource Planning
  • Infocomm Sales & Marketing Strategies
  • Risk Management
  • Spreadsheet Engineering

World Issues: A Singapore Perspective^ (2 Credit Units)

Agile DataOps (4 Credit Units)

This module explores the end-to-end cycle of data analytics through a DataOps framework. Students will be introduced to the motivations behind DataOps such as the Agile framework, how DataOps can add significant value to analytics development and deployment, and also the best practices in DataOps. Indicative topics include Agile Data Warehousing, Innovation for DataOps and Test Automation.

Machine Learning (4 Credit Units)

This module allows students to use leading software and associated libraries, to develop supervised learning and unsupervised learning models in order to solve the real life problems. Emphasis will be placed on machine learning model selection, training and development of predictive models and model evaluation. Indicative topics include Supervised Learning Models, Unsupervised Learning Models and Model Evaluation and Improvement Techniques.

Distributed Data Pipelines (4 Credit Units)

This module will introduce various aspects of data engineering concepts through the building of resilient distributed databases, such as Hadoop and Spark platforms. Students will understand how to extract valuable data from multiple sources and propose scalable solutions where appropriate. Indicative topics include Tools and Platforms for Big Data, Structures and Schemas for Big Data and Streaming Tools and Platforms.

Fundamentals for IT Professionals 3 (2 Credit Units)

This module provides a stepping stone to the students in their IT career. Students are given an insight into the infocomm industries and are kept abreast of the updates and the necessary skill sets required in their IT career path. They also have the opportunity to be exposed to the various institutes of higher learning to further enhance their skill sets.

Elective Module 2# (4 Credit Units)

Electives offered by the Diploma in Data Science:

  • Accounting
  • Advanced Databases
  • Applied Analytics
  • Banking Applications and Processes
  • Cloud Architecture & Technologies
  • Customer Decision-Making & Negotiation Skills
  • Customer Experience Management
  • Deep Learning
  • Enterprise Business Processes
  • Enterprise Resource Planning
  • Infocomm Sales & Marketing Strategies
  • Risk Management
  • Spreadsheet Engineering

Emerging Trends in Data Science (2 Credit Units)

This module explores the latest trends and technologies in the areas of Data Science, Artificial Intelligence (AI) and Data Analytics. Students will be exposed to fresh developments and prominent discoveries in machine learning techniques, growth in the areas of AI solutions, as well as ethical issues and governance pertaining to the use of AI solutions and Data Science applications.

Data Science Capstone Project (8 Credit Units)

This module requires students to complete a substantial Data Science project that is the culmination of the concepts learned and skills picked up in the first two years of the course. The project enables students to apply and integrate what they have learnt and give them an opportunity to experience the end-to-end Data Science workflow, and allowing them to delve deeper into topics of interest.

Industry Currency Project (8 Credit Units)

This module requires students to embark on Data Science projects that are contributed by the industry, allowing students to work on real-world Data Science problems yet in an academic environment, with learning facilitated by academics. Depending on the scenarios crafted in collaboration with the industry, the module may be structured as a datathon for rapid ideation and solutioning. Short workshops may be conducted to allow students to be supplemented with knowledge useful for the projects, or to be familiar with tools and understanding more about what are the resources currently available.

Elective Modules# (4 Credit Units per module)

Electives offered by the Diploma in Data Science:

  • Accounting
  • Advanced Databases
  • Applied Analytics
  • Banking Applications and Processes
  • Cloud Architecture & Technologies
  • Customer Decision-Making & Negotiation Skills
  • Customer Experience Management
  • Deep Learning
  • Enterprise Business Processes
  • Enterprise Resource Planning
  • Infocomm Sales & Marketing Strategies
  • Risk Management
  • Spreadsheet Engineering

Project ID: Connecting the Dots (IS)^ (4 Credit Units)

Internship/Project (20 Credit Units)

This module provides students with the opportunity to apply the knowledge and skills gained to develop an IT solution to solve a practical problem. Students may undertake an in-house industry-driven project, a Technopreneurship Enterprise project or a real-life IT project in a local or overseas organisation. These projects may include problem definition, requirements analysis, design, development and testing, delivery and presentation of the solution. Through the project, students will learn to appreciate the finer points of project planning and control issues relating to IT project development.

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