Applied Business Analytics in Production: Python Specialisation

Singapore University of Social Sciences

Applied Business Analytics in Production: Python Specialisation (ANL560)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: English

Duration: 6 months

Fees: To be confirmed

Area of Interest: Business Administration

Schemes: To be confirmed

Funding: To be confirmed

School/Department: School of Business

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Synopsis

This course comprises an in-depth understanding of designing and deploying business analytics systems end-to-end. The course covers crucial aspects such as project scoping, data needs, modelling strategies, and deployment patterns and technologies. Students will learn strategies to address common production challenges, including establishing a model baseline, handling concept drift, and performing error analysis. A comprehensive framework will be adopted for developing, deploying, and continuously improving a productionised analytics application. While understanding analytics concepts is essential, building an effective career in business analytics requires experience in preparing projects for deployment. This course combines foundational analytics concepts with the skills and best practices of modern software development, enabling students to successfully deploy and maintain analytics systems in real-world environments.

Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

Introduction to Business Analytics in Production
Project Scoping and Stakeholder Management
Data Needs and Collection Methods
Data Cleaning and Transformation
Feature Engineering
Modelling Strategies
Evaluation Metrics
Deployment Patterns and Technologies
Handling Concept Drift
Error Analysis and Continuous Improvement
Performance Monitoring in Production
Case Studies and Real-World Applications

Learning Outcome

Evaluate the entire lifecycle of an analytics project from scoping to maintenance
Critique essential techniques for data cleaning, transformation, and feature engineering
Assess various modelling strategies and evaluation metrics for effective model building and assessment
Formulate deployment strategies and technologies for transitioning models to production environments
Design methods to address common production challenges like concept drift and error analysis
Create objectives, identify stakeholders, and develop project roadmaps
Construct and optimise machine learning models using Python
Implement and monitor performance systems for models in production environments
Appraise and refine error analysis and continuous improvement practices