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For more information about this programme, please contact:
NTU PACE
Email: [email protected]


 

 

FlexiMasters in Data Science Fundamentals

 

This FlexiMasters in Data Science Fundamentals is suitable for learners with a STEM background who want to learn about data driven approach to problem solving that involves the process of collecting, managing, analysing, explaining and visualising data and result analysis. Upon completion of the courses, learners will have a strong understanding of the fundamental concepts and techniques that are used in the field of data science.

Learners who meet the minimum requirements will be able to pursue their Master of Science (MSc) in Data Science to professionally upskill in support of their career advancement. 

At the end of the course, learners will be able to: 

  • Develop a data science mindset to solve complex problems using data driven approaches.
  • Build strong foundations in data systems and effective data management practices.
  • Gain hands-on understanding of machine learning techniques and real world applications.
  • Apply core data science and machine learning competencies through Industry relevant courses methodologies.
  • Solve real world data problems and applications using practice oriented analytical approaches.
  • The programme consists of five courses worth a total of 15 Academic Units (AUs). 
  • Assessment(s) will be conducted during every course and learners will be graded based on their performance in the assessment(s).
  • As the micro-credential courses are only offered in this FlexiMasters in Artificial Intelligence Fundamentals programme, learners are required to enrol into this programme and complete all required courses within this programme.
  • Mode of class delivery: Classroom.

Upon successful completion, the following qualifications will be awarded:

  • A Graduate Certificate will be awarded to learners attaining 6 AUs, with a minimum Grade Point of 2.5 (which is equivalent to a letter grade of C+) achieved for each course.
  • A FlexiMasters will be awarded to learners attaining 15 AUs, and achieving a minimum Grade Point of 2.5 (which is equivalent to a letter grade of C+) for each course.

Pathway to the Master's programme: 

Credits earned are valid for 5 years for transfer of credits to the MSc in Data Science. The minimum Grade Point eligible for transfer of credits to the MSc in Data Science is 2.5 (which is equivalent to a letter grade of C+). Transfer of credits is by application and the application will be assessed and approved by the University in accordance with University Credits Transfer and Course Exemption Policy.

    To meet the requirement of SkillsFuture Singapore, assessment(s) will be conducted during every course.
    The assessment(s) include:

    1. Data Science Thinking
    - Quizzes
    - Project

    2. Machine Learning: Methodologies and Applications
    - Assignment
    - Project
    - Quiz

    3. Data Systems
    - Project
    - Quizes

    4. Data Preparation
    - Project and Presentation
    - Quizzes

    5. Data Visualisation
    - Project and Presentation
    - Quizzes

     

    This course is suitable for learners working in industries that are currently employing or intending to deploy data science techniques to improve their work processes. Prior knowledge in STEM field will be an advantage. 

    As the micro-credential courses are only offered in this FlexiMasters in Artificial Intelligence Fundamentals programme, learners are required to enrol into this programme and complete all required courses within this programme.

    Course title Objective

     

    Data Science Thinking
    (3 AU)

    This course introduces the foundations of data science and its dynamic ecosystem allowing learners to explore core concepts, ethical frameworks, and market mechanisms that shape data-driven environments. Integrating insights from personality and social psychology to interpret human behaviour, these perspectives will also be applied to data science challenges. Emphasis is placed on responsible decision making and understanding economic principles such as demand, supply, and price adjustments within data ecosystems. By the end of the course, learners will be equipped to design high level, practical solutions to real world data science problems, combining technical knowledge with behavioural and ethical considerations. 

    At the end of the course, learners will be able to:

    • Describe the notion of data science.
    • Describe data science ecosystem.
    • Apply theories and methods in personality and social psychology to understand human behavior to address data science problems.
    • Apply ethical principles and established frameworks to make responsible ethical decisions towards building a robust data science ecosystem.
    • Integrate demand, supply, price adjustment mechanism and market outcomes in data science ecosystem.
    • Design high level solutions to real world data science problems. 

     

    Machine Learning Methodologies and Applications
    (3 AU)

    This course provides a practical introduction to machine learning, covering key concepts in supervised, unsupervised, and reinforcement learning. Learners will understand the notations and formulations of machine learning problems and gain hands-on experience applying loss functions using widely used Python libraries such as SciKit Learn and SciPy. The course emphasises both theory and application, helping learners evaluate the motivations behind machine learning models and interpret insights from real world scenarios. By the end of the course, learners will be equipped with foundational skills to structure problems, implement solutions, and critically assess machine learning approaches for diverse applications. 

    At the end of the course, learners will be able to:

    • Identify the key concepts of supervised, unsupervised, and reinforcement learning. 
    • Describe the notations and formulations of a machine learning problem. 
    • Apply loss functions using existing toolbox such as SciKit or Scipy. 
    • Evaluate the motivations and insights behind the machine learning problems. 

     

    Data Systems
    (3 AU)

    This course introduces the essential role of data systems in data science, focusing on how structured data supports analytics and decision making. Learners will gain practical skills in designing relational data models and applying relational algebra to organise and query data effectively. The course emphasises hands-on experience with Structured Query Language (SQL) for data extraction and manipulation, enabling learners to work confidently with real world datasets. Additionally, learners will explore how transactions are managed in data systems, ensuring data integrity and consistency. By the end of the course, learners will be equipped to design, query, and manage robust data systems for data driven solutions. 

    At the end of the course, learners will be able to:

    • Explain the importance of data systems in data science.
    • Design the relational data model and algebra. 
    • Apply the structured query language (SQL) for data science. 
    • Explain how transactions in data systems are managed. 

     

    Data Preparation
    (3 AU)

    This course equips learners with essential skills for data preparation, a critical step in data science workflows. Learners will explore techniques for data discovery, validation, and cleaning to ensure accuracy and reliability. The course also covers methods for structuring, enriching, and filtering data, enabling learners to transform raw datasets into actionable insights. Through practical exercises, learners will apply these techniques to real world data science problems, gaining hands on experience in preparing data for analysis and modelling. By the end of the course, learners will be able to design and implement robust data preparation strategies that enhance the quality and effectiveness of data driven solutions. 

    At the end of the course, learners will be able to:

    • Explain the importance of data preparation in data science. 
    • Describe various techniques for data discovery. 
    • Identify and apply various data validation and cleaning techniques. 
    • Describe various data structuring, enrichment, and filtering techniques. 
    • Apply data preparation techniques to data science problems. 

     

    Data Visualisation
    (3 AU)

    This course provides a comprehensive introduction to data visualisation, focusing on transforming complex datasets into clear, insightful visuals. Learners will explore different data types and effective visual encoding techniques, while designing and evaluating plots and charts commonly used in data communication. Emphasising on principles of human visual perception to create visuals that resonate with audiences, learners will gain hands on experience applying techniques for interactive exploration, statistical analysis, and visualisation of abstract, scientific, and geographical data. By the end of the course, learners will be equipped to select and implement appropriate visualisation strategies that enhance understanding and decision making in data driven contexts. 

    At the end of the course, learners will be able to:

    • Describe the various types of data and its effective visual encoding in data visualisation. Design and evaluate appropriate plots and charts commonly used to visually communicate insightful information embedded in different datasets. 
    • Explore and explain how human visual perception characteristics can be considered in the design of effective visuals for the human audience. 
    • Apply appropriate techniques to visualise, interact and statistically explore data. 
    • Describe and apply appropriate data visualisation techniques to handle abstract, scientific and geographical datasets. 

    Venue: NTU Main Campus, Virtual (Online)
    Date: 

    COURSE TITLE

    CLASS SCHEDULE

    		REGISTRATION CLOSING DATE
    Data Science Thinking  
    Machine Learning: Methodologies and Applications

    Data Systems


    Data Preparation


    Data Visualisation
    		 

    REGISTER YOUR INTEREST

    Listed courses are:

    • Credit-bearing and stackable to Graduate Certificate in Data Science Fundamentals (6 AU), FlexiMasters in Data Science Fundamentals (15 AU) and MSc in Data Science (30 AU).

    Note: NTU reserves the right to change the date, venue, and mode of delivery due to unforeseen circumstances.

      These courses are part of:

      • Graduate Certificate in Data Science Fundamentals (6 AU)
      • FlexiMasters in Data Science Fundamentals (15 AU)
      • MSc in Data Science Fundamentals (30 AU)

      Learners will receive their Statement of Accomplishment (for a grade of D and above) or Certificate of Participation for each course—dependent upon their assessment performance.

         

        Programme Fee: S$31,610.00 (inclusive of GST)

        BEFORE funding & GSTAFTER SSG funding
        (if eligible under various schemes)
        & 9% GST
        SSG Funding SupportProgramme FeeCourse FeeProgramme Fee PayableCourse Fee Payable

        Singapore Citizen (SC) and Permanent Resident (PR)

        (Up to 70% funding)

        		$29,000.00$5,800.00Pending SSG approvalPending SSG approval
        Enhanced Training Support for SMEs (ETSS)$29,000.00$5,800.00Pending SSG approvalPending SSG approval

        Singapore Citizen aged ≥ 40 years old SkillsFuture Mid-career Enhanced Subsidy (MCES)

        (Up to 90% funding)

        		$29,000.00$5,800.00Pending SSG approvalPending SSG approva

        • NTU/NIE alumni may utilise their $1,600 Alumni Course Credits for each course. Click here for more information.
        • Learners can utilise their SkillsFuture Credits for these courses.
        • Singaporeans aged 40 years and above are able to use their SkillsFuture Credit (Mid-Career) top-up of $4,000 to offset the course fees after SSG funding.

         

         

        Assoc Prof Melanie Herschel
        Instructor for: 
        Data Science Thinking, Data Systems

        Assoc Prof Melanie Herschel is an Associate Professor at the College of Computing and Data Science (CCDS), NTU. Before joining the College of Computing and Data Science at Nanyang Technological University in 2024, she was a Professor for Data Engineering at the University of Stuttgart and an Associate Professor and INRIA team member at the University Paris Saclay, France. She obtained her doctorate from Humboldt-University Berlin, Germany. Her research interest is in the data management areas of data integration, data quality, data provenance, and data analytics that aspires to get data in shape for a large variety of applications in an effective, resource efficient, user-friendly, and responsible way.

         

         

        Assoc Prof Sourav S Bhowmick
        Instructor for: 
        Data Science Thinking, Data Preparation

         

        Assoc Prof Sourav S Bhowmick is an Associate Professor at the College of Computing and Data Science (CCDS), NTU. He leads the Data Management Research Group at NTU (DANTE). He is also the Research Group Lead of Data Management & Analytics Group in College of Computing & Data Science (CCDS). He was inducted into Distinguished Members of the ACM in 2020 for "outstanding scientific contributions to computing". His  core research expertise is in data management, human-data interaction, and data analytics.

         

         

        Prof Zhang Hanwang
        Instructor for: 
        Machine Learning Methodologies and Applications

        Prof Zhang Hanwang is a faculty at the College of Computing and Data Science (CCDS), NTU. He has received the B.Eng (Hons.) degree in computer science from Zhejiang University, Hangzhou, China, in 2009, and the Ph.D. degree in computer science from the National University of Singapore in 2014. He was a research scientist at the Department of Computer Science, Columbia University, USA and a senior research fellow at the School of Computing, National University of Singapore, Singapore. His research interests include computer vision, multimedia, and social media. 

         

        Assoc Prof Long Cheng
        Instructor for: 
        Data Systems

        Assoc Prof Long Cheng is a Nanyang Associate Professor at the College of Computing & Data Science (CCDS), Nanyang Technological University. He received his PhD degree from the Hong Kong University of Science and Technology, Hong Kong. From 2016 to 2018, he was a lecturer at Queen's University Belfast, UK. He has research interests broadly in data management and data mining, such as high dimensional vector data management, spatial data management with machine learning based techniques, spatial data mining in the urban domain, and graph data mining. 

         

        Assoc Prof Goh Wooi Boon
        Instructor for: 
        Data Visualisation

        Assoc Prof Goh Wooi Boon is the Associate Dean (Continuing Education - FlexiMasters & Short Courses) at the College of Computing and Data Science (CCDS), NTU. Before joining NTU, he was senior engineer and later engineering section manager at the Mechanization and Automation department of Hewlett Packard Singapore. His industrial engineering expertise is in developing robot-assisted automation systems. Assoc Prof Goh’s research interest is in human computer interactions.