Data Science Modelling with Programming

Overview

Part of the ModularMaster in Data Science programme

In the 21^st century, we continue to see a rising trend in the applications of Data Science (DS) from the use of GrabPay to the example of Siri. The applications of Data Science encompass all walks of life and indirectly every aspect of the business – from how business acquires data to how data contributes and complements with the traditional intuitive decision-making approach. Data Science will not only continue to shape how industries operate in the near future but also revolutionise how firms harness Data to its full potential. Data Scientist has been hailed the “sexiest career in the 21^st Century“, however, not only are firms competing to hire good data scientists, but are also starting to see the need to groom their in-house Subject Matter Experts into a functional-hybrid kind.

We will focus on how Data Science is being used across a wide spectrum of industries. We will also explore various use cases and applications of Data Science and its potential for improving our daily lives. Participants will be able to leverage on Data Science to complement with their Domain Expertise to contribute within the Data Science Pipeline. Participants will be equipped with the Basics of Data Science Modelling and Algorithm with Programming.

At the end of this course, participants will be able to apply basic Predictive Analytics, basic Machine Learning and basic Statistical Model based on a prescribed project.

Plan your learning path

This course can be taken as a module on its own or as part of the Graduate Certificate in Data Analytics (Programming) stack and participants will earn 12 subject credits which can also be used towards completing the ModularMaster in Data Science.

Course Details

Course Dates:
Currently unavailable

Duration:
5 days, 9:00 am – 5:00 pm

Who Should Attend

• Learners especially those working in an industry or role dealing with data, who would benefit from data science modelling with the use of programming methods.

Prerequisites

• Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
• Participants should preferably have basic knowledge of statistic.
• Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
• Participants are encouraged to complete the Data Wrangling and Preparation with Programming and Data Validation and Statistical Analysis with Programming 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).
• Participants are required to bring their laptops.