Data Analytics Process and Best Practice II
About This Course
An organisation's analytics potential depends on its capability to process and manage the acquired data (both internal & external). The credibility and usefulness of sophisticated data analytics solutions rest upon good quality data. However, good, clean data cannot always be readily available.
In the era of big data, analytics, and advanced technology, sophisticated models can be built and deployed quickly. This course brings the focus back to the fundamentals of exploring, cleaning, preparing the data, and the process of data acquisition and management. These processes ensure that the advanced analytics models are built on a strong foundation of data analytics process.
This course has been designed to equip analytics professionals and managers with an understanding of data analytics processes so that their analytics activity downstream will be more credible and useful. This course is part of the Data Science, Graduate Certificate in Business Analytics Practice Series offered by NUS-ISS.
What You'll Learn
Understand an end to end view of data analytics process.
Structure a framework to align analytics objectives with business goals.
Apply procedures and techniques for data sampling, data cleaning & audit.
Apply procedures and techniques for data transformation, exploration, model testing and evaluation.
Understand basics of data warehousing.
Design a data pipeline process.
Design strategies for implementation of analytics projects.
Entry Requirements
This is an intensive, intermediate course. Participants with some exposure to working with data using tools like R will benefit more from the course.
Participants with limited knowledge may consider acquiring them via Statistics in Action: Driving Data Insights course.