DATA PRODUCT II (AI/ML) CAPSTONE (Part of SCTP Certificate in Data Analytics with AI)
About This Course
In this final Capstone project, learner will be immersed in a practical and integrative experience in applying the knowledge and skills learnt throughout the entire course. Building on the techniques learnt in data transformation and machine learning/artificial intelligence, learners will be tasked to solve business scenarios by analysing and deriving insights using real-world dataset. Learner will showcase in this capstone project a final data product, infusing AI models to deliver innovative, data-driven solutions to stakeholders in this end-to-end project development.
What You'll Learn
> Propose Problem Statement and Data Product plan to solve business challenges.
> Construct Extract-Transform-Load (ETL) pipelines to process data from various sources using programming.
> Apply Machine Learning techniques to extract insights from data.
> Implement Data pipeline via Continuous Integration/Continuous Development (CI/CD).
> Deploy Data Product with AI Models to resolve the business challenges.
This course is part of SCTP Data Analytics with AI
1) The list of topics covered in this course:
> Problem Statement Framing
> Data Transformation
> Machine Learning/Artificial Intelligence
> Data Product Design
> DevOps CI/CD
(2) Instructional Methods:
> Supervised Field and Practical Training
> Capstone Development via Agile Methodology
> One-year Udemy subscription offering access to curated course titles, enabling self-directed learning to complement formal training. Learners can explore and develop skills at their own pace through this comprehensive online learning platform.
(3) Duration of Course:
200 hours
Entry Requirements
Applicants for the course should possess a diploma/degree from a local polytechnic/university or is a ITE graduate with minimum of 2 years working experience, preferably in a related field. Holders of other equivalent academic qualification from foreign institutions will be considered on a case-by-case basis.
Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or a proficiency test.