Upon completion of the course, students will be able to:
- Practice problem solving skills.
- Read UML sequence and class diagrams.
- Apply basic concepts of Object Orientation to a given scenario/context.
- Apply good programming practices and design concepts to develop software.
- Appreciate the role of algorithms and in problem solving.
Upon completion of the course, students will be able to:
- Apply the key object-oriented programming and design techniques of abstraction, encapsulation, inheritance and polymorphism to a given scenario.
-Sketch UML class diagrams and sequence diagrams.
-Create and debug programs using the Java programming language.
- Apply good programming practices and design concepts to develop software.
- Integrate object-oriented thinking into application of problem-solving skills.
- Appreciate the role of algorithms and data structures in problem solving.
This course aims to provide students with a broad coverage and examples of social analytics techniques and trends underlying the current and future development. Upon completion of the course, students will be able to:
- Extract social media data via social APIs and custom scripts.
- Extract social networks from non-network data such as transactional/operation data as well as textual conversations.
- Computationally identify and quantify social influencers.
- Computationally extract and identify trending topics.
- Visualize social networks and text analysis results.
- Deploy custom scripts in Amazon Web Services.
This course aims to provide students with a broad coverage and examples of social analytics techniques and trends underlying the current and future development. Upon completion of the course, students will be able to:
- Extract social media data via social APIs and custom scripts.
- Extract social networks from non-network data such as transactional/operation data as well as textual conversations.
- Computationally identify and quantify social influencers.
- Computationally extract and identify trending topics.
- Visualize social networks and text analysis results.
- Deploy custom scripts in Amazon Web Services.
This course aims to provide students with a broad coverage and examples of enterprise analytics techniques with special focus on supervised machine learning techniques and applications. Upon completion of the course, students will be able to:
- Work with data from exploration to pattern discover to deployment
- Prepare the data and create new powerful features
- Build powerful machine learning models efficiently
- Assess each supervised model using the appropriate criterion
- Apply robust supervised algorithms such as decision trees, gradient boosting models, forests, neural networks and support vector machines.
- Develop expertise in using SAS machine learning tool called Model Studio in SAS Viya
- Build Machine Learning pipelines in SAS Model Studio
- Deploy and manage machine learning models in production
Upon completion of the course, students will be able to:
- Gain broad awareness of care landscape and ecosystem in Singapore.
- Understand the importance of, and factors leading to smart healthcare.
- Conceptualise and design an end-to-end technological solution to solve a relevant healthcare problem.
- Gain good understanding of constraints and limitations of operationalising smart healthcare.
Upon completion of the course, student will be able to:
- Showcase expertise in executing a project using knowledge acquired from the courses taken from the IS curriculum.
- Experience developing of some technology deliverable for an IT system or proof of concept.
- Experience working in a team environment with a sponsored project (internal, external or self-proposed) using project management skills experience throughout the courses taken in IS.
- Learn about an industry or technology that is related to his selected track not otherwise available in the course curriculum.
- Work on complex and real project used by the project sponsor.
By the end of this course, students will learn to:
- Cleanse and prepare data to be in a form adequate for machine learning.
- Outline the steps involved in developing and consuming a machine learning model.
- Develop adequate machine learning models to meet different business objectives.
- Tune the performance of machine learning models.
- Create visualizations to consume the output from machine learning models.
By the end of this course, students will learn to:
- Cleanse and prepare data to be in a form adequate for machine learning.
- Outline the steps involved in developing and consuming a machine learning model.
- Develop adequate machine learning models to meet different business objectives.
- Tune the performance of machine learning models.
- Create visualizations to consume the output from machine learning models.
Upon completion of the course, students will learn:
System development infrastructure
✓ Source code management with Git and GitHub
✓ Task Management with ClickUp
✓ Free document system, Confluence
Data crawling and extraction framework
✓ Extract information from web pages with Scrapy
Data storage system
✓ Choose the right storage architecture based on data characteristics
✓ Use data store and document store for various types of data
Massive data processing frameworks
✓ Install and deploy Hadoop and Spark
✓ Program big data processing logics with Hadoop and Spark
Data interface between modules
✓ Process JSon files
✓ Adopts GraphQL as the data interface
Other open source big data tools
✓ Visualise data results with D3.js
✓ Monitors online data with Prometheus
