NICF- Data Analytics Process and Best Practice (SF)
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This course requires participants to have knowledge of R
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. But good, clean data cannot always be readily available.
This course has been designed to equip analytics professionals and managers with an understanding of Data Analytics Process and Best Practices so that their analytics activity downstream will be more credible and useful.
Over a period of 3 days ,this course will provide participants with a practical understanding of Data Analytics Process and Best Practices.
The course provides an intermediate pathway into technical ‘methods’ courses in the ISS portfolio (such as those in the ISS-NICF Enterprise Business Analytics syllabus).
This course is part of the Stackable Certificate Programme in Business Analytics offered by NUS-ISS.
At the end of the course, the participants will be able to:
- 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, feature selection and generating data sets for model build & test, including data balancing.
- Propose and conduct statistical techniques for data exploration and modeling.
- Understand the methods and metrics involved in model testing and evaluation
- Understand the issues and methods involved in model deployment
- Advice on code of conduct to be adhered for prudent data management.
What will be covered
- Business Analytics Framework & Process
- Identifying and Eliciting Goals
- Data Collection and Survey Sampling
- Data Exploration
- Data Cleaning & Preparation
- Model Building Process
- Model Deployment
Who should attend
This is an intermediate course and is applicable for professionals engaged in the following areas:
- Data Analysts
- Research Analysts
- Data Scientists
- Analytics Consultants
- Analytics Engineers
- Knowledge Engineers
- AI professionals