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N81

Diploma in

Data Science

 

Build a solid foundation in Data Analytics and Machine Learning to prepare you for the future economy


Gain exposure to emerging industry trends and real-world experience in our one-year Industry Kickstart Programme


Pursue additional industry certifications offered by industry leaders such as Microsoft, Salesforce, Dataiku and Tableau


Be trained by the school that co-founded the Data School in Singapore​





​​Course Overview​



Curious about why I cho​se DS​? Watch this!


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What You'll Learn

Year 1

  • 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)
^ Interdisciplinary Studies (IS) modules account for 13 credit units of the diploma curriculum. They include modules in communication, innovation and world issues, as well as an interdisciplinary project. By bringing students from diverse diplomas together, the interdisciplinary project fosters collaboration to explore and propose solutions for real-world problems. IS aims to develop students to be agile and self-directed learners, ready for the future workplace.

For more details on Interdisciplinary Studies (IS) electives
Click Here >

Year 2

  • 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)
# The elective modules offered may change from year to year, depending on relevance and demand. They may also include modules available in other diplomas offered by the School.
^ Interdisciplinary Studies (IS) modules account for 13 credit units of the diploma curriculum. They include modules in communication, innovation and world issues, as well as an interdisciplinary project. By bringing students from diverse diplomas together, the interdisciplinary project fosters collaboration to explore and propose solutions for real-world problems. IS aims to develop students to be agile and self-directed learners, ready for the future workplace.

For more details on Interdisciplinary Studies (IS) electives
Click Here >
  • 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
# The elective modules offered may change from year to year, depending on relevance and demand. They may also include modules available in other diplomas offered by the School.

Year 3


Career Options & Further Studies

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. You can look forward to roles such as data analyst, associate data engineer and associate business analyst.

You may receive advanced standing when you apply for related degree programmes at local universities. You can also look forward to pursuing a specialist diploma in an analytics-related field and other advanced diploma courses at local polytechnics.​




Hear From Our ​Students And Alumni

Entry Requirements

FOR STUDENTS WITH O-LEVEL EXAMINATION RESULTS

AGGREGATE TYPE ELR2B2-C

Candidates must have the following GCE 'O' Level examination (or equivalent) results.

  • English Language

    1 - 7
  • Mathematics (Elementary/Additional)

    1 - 6
  • Any two other subjects

    1 - 6

You must also have sat for one subject listed in the 2nd group of relevant subjects for the ELR2B2-C Aggregate Type.

ELR2B2-C Aggregate Type List >

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


FOR STUDENTS WITH OTHER QUALIFICATIONS

Find out more on entry requirements and admissions exercise periods for qualifications such as N(A)-Level, A-Level, ITE, IP, IB and more.

Click Here >

Application Information

  • Range of Net ELR2B2 for 2021 JAE

    5​ to 11
  • Planned Intake (2021)

    50

ADMISSIONS EXERCISE

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