MLOps (Synchronous E-learning)
About This Course
This course develops learners’ ability to analyze, evaluate, and apply advanced data science and machine learning techniques in business contexts. It combines conceptual knowledge, assessed through written exams, with practical application, demonstrated in projects. Learners progress from differentiating ML and deep learning methods and understanding the MLOps lifecycle, to performing exploratory data analysis, evaluating and maintaining models, interpreting data patterns, and applying statistical and data mining techniques. The course culminates in demand forecasting and business insights, preparing learners to critically analyze forecasts, interpret results, and make actionable recommendations that support organisational decision-making.
What You'll Learn
Analyse business problems using exploratory data analysis to visually and analytically explore datasets, leverage available data, and exploit business data to extract insights.
Differentiate statistical techniques and data mining methods by analysing and running complex data mining models to determine their suitability for generating business insights in line with organisational procedures.
Examine time-series forecasting methods and deep learning models by analysing data patterns and how models are configured and customised to investigate organisational business hypotheses.
Interpret data patterns and model evaluation results by analysing implications for business problem scenarios, including efficiency, growth, and return on investment.
Deconstruct the MLOps lifecycle, tools, and platforms by analysing methods to manage organisational capacity, measure data science team capability, and manage data science projects.
Categorize organisational information, interpret data patterns, identify key factors, and develop recommendations for organisational decision-making.
Evaluate and apply methods for prioritising data science projects to manage project execution and communicate results effectively to stakeholders.
Entry Requirements
• Age: 25 years and above
• Education: At least a GCE 'O' Level certificate/ GCE ‘N’ Level (Normal Academic stream), Nitec or Higher Nitec qualifications
o Applicants without formal academic qualifications may also be considered if they have relevant work experience and computer knowledge
• Digital Proficiency:
o Comfortable using digital tools and workplace software (e.g., Microsoft Excel, cloud-based platforms)
o Able to perform basic computer tasks such as internet search, creating folders, and copy-paste operations
• Technical Experience:
o No coding experience is required