Diploma in Applied AI & Analytics (S30) | Singapore Polytechnic

Provides students with the fundamental concepts in Artificial Intelligence (AI) and Machine Learning. The module aims to provide students with hands-on experience in building applications that use machine learning and neural networks. The students will also learn skills to build intelligent agents, such as Chatbots and integrate cognitive service APIs to add intelligence into their applications.

Learn the broad overview of Artificial Intelligence (AI) and its societal and ethical impact on modern society. Build a simple AI model with a drag-and-drop tool and consider how the use of AI can address a chosen SDG.

Aim to equip students with the skill in developing database driven web application. Students will learn about server-side programming and be able to create-database-driven web applications using a scripting language and programming frameworks.

Find out what goes into a healthy and productive team where ideas, insights, and responsibilities are shared meaningfully. Sharpen your interpersonal and communication skills to collaborate effectively in a digital environment.

Develop data fluency skills such as formulating questions, processing and analysing data to articulate numerical and graphical summaries. Build a simple data dashboard to generate insights and support a solution for a chosen SDG.

This module aims to help students discover their strengths, values and interests. It also supports students in making informed educational and career choices to achieve their career aspirations.

Acquire the skills needed to write in clear and concise English to effectively communicate an intended message to your target audience. Apply these skills when writing for specific contexts, such as scenarios related to the SDGs.

Please refer to Elective Modules Framework for more information.

Aims to equip students with the knowledge and skills in developing effective front-end web applications using Hypertext Markup Language (HTML) and Cascading Stylesheets (CSS). Students will learn to use front-end web development frameworks to further enhance their ability for rapid prototyping responsive web application.

This module aims to provide students with an understanding of computer networking concepts and hands-on sessions on with operating systems using Command-Line Interfaces. Students will be taught on the use of various UNIX commands / system tools for user management, software installation, network administration and configuration of services. These topics are essential and prerequisite to an Application Developer for building and deployment of a software system.

Aims to help students pick up a programming language and learn how to solve and automate tasks through programming. Students will be taught programming fundamentals such as variables, data types, operators, control structures, methods and data structures such as arrays. At the end of the module, students will be competent in using programming for problem solving.

Equips students with knowledge in mathematics and analytical skills to solve problems related to infocomm technology. Topics include matrices, linear transformation, number systems, set theory, logic, Boolean algebra, techniques of counting and probability.

Appreciate the use of both creative and computational thinking skills to solve a SDG-related problem. Apply focused and diffuse thinking , brainstorming techniques, and the four pillars of computational thinking to a chosen SDG.

Provides students with the fundamental skills to code applications to retrieve, manipulate, process and analyze data using the Python programming language. Students will learn to code in the Python Programming Language and key concepts such as exploratory data analysis and perform data analysis using Numpy, Pandas and Statsmodels on the data to gain useful insights for business decisions.

Provides students with an introduction to elementary probability theory and statistical concepts and principles that lay the foundation to understand and learn the statistical procedures and methods. Topics include descriptive statistics, rules of probability, probability distributions of discrete and continuous random variables, sampling distributions, statistical estimation and hypothesis testing.

Develop critical and analytical thinking skills, along with the ability to present your point of view with credible and relevant evidence. Enhance your knowledge and appreciation of local and global issues covered in the 17 SDGs, with a focus on the former.

This module provides students with an overview of Agile methodologies and Machine Learning Operations (MLOps) to enhance the efficiency and reliability of machine learning (ML) project development and delivery. It covers essential practices for planning ML project using the Agile framework, managing data and ML models, developing ML applications with continuous integration and deployment, and monitoring of ML models in the production environment. Students will learn Agile project management, MLOps and Container tools and gain hands-on experience in automating workflows to build robust machine learning projects.

The module covers the fundamental concepts to build and work with data pipelines. Students are taught how to work with relational database, non-relational (NoSQL) database, and large data stores such as data warehouses and how to integrate data from multiple data sources into a single repository using Extract, Transform and Load (ETL) workflows.

Aims to teach students advanced Object-Oriented concepts and data structures and algorithms using Python. Through this module, students will learn how to implement stacks, queues, linked lists, dictionaries and solve problems using these data structures. Algorithms to improve code efficiency and search will also be taught.

This module aims to equip students with techniques for creating impactful reports and interactive dashboards, enabling organisations to extract deeper insights from their business data. Students will learn best practices for developing effective data visualisations using popular industry tools such as Tableau and Python. By the end of this module, students will be proficient in building and deploying comprehensive visual reporting solutions that drive informed business decisions.

This module teaches students deep neural network architectures and its practical applications. Students will learn to frame problems and prepare machine trainable datasets. They will apply deep learning frameworks such as Tensorflow, Keras, and PyTorch, to train deep learning models. Eventually, students will create and train their own deep learning models in various applications such as image recognition using convolutional neural networks, natural language processing using recurrent neural networks, creative deep learning using generative AI, as well as problem solving using reinforcement learning.

Leverage digital communication to engage, inspire and impact stakeholders diligently. Learn about responsible Digital Citizenship and how you can create engaging and impactful messages through various digital platforms and tools.

Please refer to Elective Modules Framework for more information.

Please refer to Elective Modules Framework for more information.

The module equips students with key mathematical concepts in data mining, machine learning, data preparation, and model building. Students will delve into topics such as linear algebra, principal component analysis (PCA), differentiation, applications of derivatives and partial derivatives. Additionally, students will gain proficiency in numerical algorithms used to solve mathematical equations and programming skills to carry out higher-order differentiation.

Get a head start in your career with the necessary skill sets, knowledge and tools to create your very own personal brand. Learn effective strategies to set yourself apart from the competition, and put it to good use and create awareness about the SDGs.

Develop data visualisation skills and apply narrative elements and persuasive delivery techniques to communicate data effectively. Learn to craft compelling narratives, based on hard facts and data, to call stakeholders into taking action to tackle the SDGs.

Work in multi-disciplinary groups to apply design thinking methods, backed by research and qualitative data, to tackle local sustainable issues mapped to the SDGs. Tap into knowledge and skills learnt in CCC modules to create innovative prototypes for real-life issues faced by local communities.

This module provides students with an understanding of what Big Data is, why it is needed and how Big Data pipelines are being implemented using Apache Spark. Apache Spark is a powerful platform that provides ways to store and utilize big data. Students will analyze data using Hadoop & Spark SQL and demonstrate how to create a streaming analytics application using Spark Streaming.

The module introduces students to Generative AI and Large Language Models (LLM). The module offers a deep dive into Transformer architecture, the backbone of LLM. Students will extend their knowledge beyond theory, gaining practical skills in fine-tuning LLMs and developing applications using LLM frameworks like LangChain, LlamaIndex, HayStack, RAGatouille and Embedchain. They will also master techniques such as prompt tuning and prompt engineering to refine LLM output, and interface LLMs and agents with their datasets to create AI applications.

This module aims to teach students the fundamental concepts of graph theory and knowledge graphs, equipping them with the skills to effectively design graph data models, write graph query language and build graph applications. Students will learn how to apply the algorithms such as pathfinding, community detection, centrality measures to solve real-world problems. Through hands-on exploration, students will gain a deep understanding of graph traversal algorithms and their practical applications, enabling them to extract valuable insights and meaning from complex graph structures.

Provides students with work exposure to an industrial environment, with opportunity to relate what is taught in the classroom to actual work situation. It creates a valuable learning opportunity for students to sharpen their skills and knowledge, as well as contributing to the development tasks of participating organisations.

Provides students an opportunity to integrate the knowledge and technical skills they have acquired from the course, and experience the AI and Analytics workflow (Problem Framing /Data Exploration; Preparation / Data Engineering / Feature Engineering / Analytics and Computational Modelling / Data Analysis / Data Visualization), problem solving, project management, communication and working in a team to implement an AI and Analytics project. The project can be based on any AI or Analytics application area, subject to the approval of the school.

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

Provides students an opportunity to integrate the knowledge and technical skills they have acquired from the course, and experience the AI and Analytics workflow (Problem Framing /Data Exploration; Preparation / Data Engineering / Feature Engineering / Analytics and Computational Modelling / Data Analysis / Data Visualization), problem solving, project management, communication and working in a team to implement an AI and Analytics project. The project can be based on any AI or Analytics application area, subject to the approval of the school.

This module aims to provide students with work exposure to an industrial environment, with opportunity to relate what is taught in the classroom to actual work situation. It creates a valuable learning opportunity for students to sharpen their skills and knowledge, as well as contributing to the development tasks of participating organisations.