Overview
Part of the ModularMaster Certificate in Data Science & Artificial Intelligence programme
This three-day course offers hands-on practice in statistical inference and model validation using Python. It emphasises key concepts such as p-value, significance level, test statistics, critical value, and type I and II errors. Participants will learn when and how to perform parametric tests as well as non-parametric tests on data, as well as how to perform model validation to assess the appropriateness of a selected statistical model. It concludes with a comprehensive project that demonstrates the application of learned skills in a real-world scenario.
Course details
Course dates:
Currently unavailable.
Course duration:
3 days, 9.00 AM – 5.00 PM
Who should attend
This course is suitable for participants seeking to deepen their understanding of statistical testing methodologies: data science and analytics professionals, IT and technology professionals, and other professionals in data-related fields.
Prerequisites
- Participants should preferably have passed mathematics at GCE ‘O’ Level or equivalent.
- Participants should preferably have basic knowledge of statistics (descriptive and inferential statistics).
- Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
- Participants are encouraged to complete the Foundation of Data Science course before enrolling in this course.
- Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).