The curriculum begins with the Foundation in Data Analytics for the Maritime Industry course, where students learn the entire data science lifecycle, from understanding the real-world problem, to data cleaning, data visualisation, data description and making statistical inferences and predictions using simple linear regression models.

This course lays the foundation for understanding the tools and ethical considerations essential in data science, contextualised to the maritime industry. It empowers maritime professionals to leverage data analytics for better decision-making, improving efficiency and outcomes.

It adopts a blended learning approach (i.e. face-to-face workshops and e-learning). In total, there are about 12 hours of video content and 27 hours of face-to-face workshop content. Every week, three to five hours can be allocated for e-learning material, take-home assignments, project work and reviewing materials.

Pre-requisites

• Bachelor’s degree in any field preferred
• At least two years of relevant work experience preferred
• Proficiency in English, both written and spoken

Requirements for Award of Certificate

Learners are required to maintain a minimum attendance of at least 75% in the course. The Executive Certificate in Foundation in Data Analytics for the Maritime Industry will follow the NUS Grade Point Average (GPA) minimum requirement guidelines for coursework-based programmes, with a minimum GPA of 2.00 required to be awarded the certificate.

Learning Outcomes

1. Apply the Data Science Lifecycle — Understand key stages such as data cleaning, visualisation, analysis, insights and recommendations.
2. Apply Data Science Tools and Techniques — Use simple yet powerful methods to explore and visualise data for better decision-making.
3. Make Ethical and Responsible Decisions — Apply data science responsibly, ensuring fairness and transparency.
4. Master Probability Concepts for the Maritime Industry — Use probability and conditional probability to assess risks and opportunities.
5. Turn Data into Decisions — Apply hypothesis testing to evaluate workplace scenarios and make confident decisions under uncertainty.
6. Uncover Business Insights — Identify relationships between variables to spot trends and correlations in business data.
7. Make Data-Driven Predictions — Use simple linear regression to forecast outcomes and guide strategic planning.
8. Work on Real-World Industry Challenges — Apply your learning in a hands-on, problem-solving group project.

The courses in this certificate equip maritime professionals with the skills to harness the power of data visualisation and customer analytics to drive strategic decision-making. Participants will learn how to transform complex maritime datasets (such as vessel movements, cargo flows and operational performance) into clear, actionable insights through effective visualisation techniques. The courses also cover how customer analytics can be used to uncover market trends and demands, predict customer behaviour and develop targeted engagement strategies. Designed for professionals in the maritime industry, this hands-on course bridges data and business needs to enhance both operational efficiency and customer satisfaction.

By harnessing the power of R for data manipulation, segmentation and predictive modelling, this course empowers maritime professionals to unlock actionable insights that enhance logistics, elevate customer satisfaction and sharpen competitive advantage. Participants will gain hands-on experience applying key data science techniques within the context of maritime operations, equipping them to drive informed, data-driven strategies that make a measurable impact.

Pre-requisite

• Executive Certificate in Foundation in Data Analytics for the Maritime Industry

Requirements for Award of Certificate

Learners are required to maintain a minimum attendance of at least 75% in each course. The Executive Certificate in Intermediate Applied Data Science for Maritime Industry follows the NUS GPA minimum requirement guidelines for coursework-based programmes, with a minimum GPA of 2.00 required to be awarded the certificate.

Learning Outcomes

1. Visualisation Best Practices — Identify and apply best practices in maritime data visualisation to communicate insights and avoid common pitfalls.
2. Data Pre-Processing and Preparation — Confidently manipulate and prepare maritime datasets in R to uncover trends, patterns and operational insights.
3. Data Visualisations — Create impactful visualisations in R to showcase key maritime metrics such as vessel movements, cargo volumes and operational performance.
4. Data Segmentation — Segment complex maritime datasets using clustering techniques and present insights through compelling visualisations.
5. Data Clustering — Group maritime customers into meaningful segments to tailor and optimise targeted marketing strategies for enhanced engagement and retention.
6. Segmentation Analysis — Analyse and segment high-value maritime customers by examining loyalty and transaction patterns to drive targeted engagement strategies.
7. Strategic Promotions — Determine the ideal timing for promotional strategies in the maritime sector to maximise impact and customer engagement.
8. Market Basket Analysis — Analyse and identify frequently purchased maritime product and service combinations to optimise offerings and drive sales growth.
9. Business Application — Generate actionable, data-driven recommendations to improve maritime customer analytics and inform strategic decision-making.
10. Work on Real-World Industry Challenges — Apply your learning in a hands-on, problem-solving group project.

Surveys made widespread by the Internet are a common tool for organisations to understand broader populations without needing individual assessments. The courses in this certificate cover essential tools for conducting survey projects, beginning with questionnaire design and relevant statistical background. We will explore three common sampling designs fundamental for large-scale surveys, delve into factor analysis for estimating unobservable traits like attitudes and examine natural language processing techniques for analysing open-ended questions, offering deeper insights than simple frequency counts or word clouds. Utilising the statistical computing language R, which offers extensive packages for survey analytics, participants will gain a comprehensive understanding of modern survey methodologies.

Optimisation involves finding the best decision variables considering constraints to optimise an objective function. There are various problem categories based on variables, constraints and objectives, with specific techniques developed for each.

Learners will be introduced to linear and integer programming, implemented using R. Linear programming optimises linear objectives with linear constraints, focusing on the simplex method and sensitivity analysis. Integer programming deals with integer-restricted variables, covering the branch-and-bound and heuristic approaches. The course delves into specific techniques tailored to each problem type, equipping learners with a comprehensive understanding of optimisation strategies for diverse scenarios.

Survey Analytics (Maritime) addresses the critical role of surveys in organisational decision-making, reflecting their prevalence and the need for skilled professionals in their design, administration and analysis. It offers a comprehensive exploration of the survey process, from questionnaire design to data analysis, incorporating techniques like factor analysis and natural language processing to enhance data gathering quality and efficiency. Utilising R for its robust survey analytics capabilities, the course prepares participants for modern survey methodologies, aiming to improve their ability to extract actionable insights and effectively understand diverse groups, ensuring surveys remain a potent tool for organisational intelligence.

In addition, Optimisation for Decision-Making (Maritime) is grounded in the fundamental need to equip learners with essential skills for effective decision-making in various domains. Optimisation techniques play a crucial role in identifying the best possible choices from a set of alternatives while considering constraints and objectives. By offering insights into linear and integer programming using the R programming language, the course aims to empower individuals to navigate complex decision-making scenarios efficiently. The inclusion of the simplex method, sensitivity analysis, branch-and-bound algorithm and heuristic approaches reflects the course's comprehensive approach towards addressing both small and large-scale optimisation problems. Ultimately, the course seeks to provide practical solutions and strategic insights that can enhance decision-making processes across industries and disciplines.

The curriculum of the certificate equips maritime professionals with survey analytics and optimisation techniques, leveraging R for structured data analysis and optimisation modelling to enhance operational efficiency, support strategic planning and data-driven decision-making.

Pre-requisite

• Executive Certificate in Intermediate Applied Data Science for the Maritime Industry

Requirements for Award of Certificate

Learners are required to maintain a minimum attendance of at least 75% in each course. The Executive Certificate in Advanced Applied Data Science for the Maritime Industry follows the NUS GPA minimum requirement guidelines for coursework-based programmes, with a minimum GPA of 2.00 required to be awarded the certificate.

Learning Outcomes

1. Appraisal of the Main Phases of Survey Deployment within the Maritime Industry — From designing a questionnaire to choosing a representative sample of seafarers, port operators or maritime stakeholders, and analysing the responses for insights into maritime survey categories/factors that are well liked/not liked, to the evaluation of the survey design for potential issues or shortcomings that might have affected the responses.
2. Evaluation of Maritime Survey Designs — To evaluate whether a survey form used in the maritime sector (e.g. crew satisfaction, employee satisfaction, vessel performance or port service feedback) is well-designed and whether the sampling techniques and estimation of the survey sample size are correct.
3. Critique of the Use of Sampling Design in Maritime Datasets — To assess how appropriate sampling strategies (e.g. how post-stratification sampling and weighting class adjustments across vessel types or ports are performed) can improve the quality of the data analysis in large maritime populations.
4. Evaluation of Factor Analysis in Maritime Datasets — To defend the use of factor analysis as a method to reduce complex maritime survey datasets (e.g. on shipboard, working conditions or crew emotional health) into fewer, more interpretable dimensions such as fatigue, port efficiency or workplace well-being.
5. Evaluation of Sentiment Analysis in Maritime Communication — To evaluate how sentiment analysis and topic modelling can be used to extract meaningful insights from open-ended survey responses or incident reports submitted by crew members or port staff.
6. Knowledge in Optimisation for Maritime Decision-Making — Acquire knowledge of optimisation techniques relevant to the maritime industry, such as the transportation problem, the travelling salesman problem or resource allocations.
7. Application of Optimisation Techniques to Maritime Problems — Appropriately apply optimisation models in R to solve real-world maritime problems, including route optimisation, fuel efficiency improvement or reduction of carbon dioxide emissions.
8. Interpret Optimisation Results for Maritime Decision-Making — Derive practical insights from optimisation outputs to support data-informed decision-making in areas such as fleet management, port scheduling or maritime logistics.