Introduction to Data Analytics for Built Environment

Training Provider: SINGAPORE INSTITUTE OF MANAGEMENT GROUP LIMITED
Course Reference: TGS-2024042979
S$234
Original: S$780
Save S$546

About This Course

Data Science is a process that involves working with different stakeholders, and working through the different processes to create a model using algorithms to assist them to analyze the data and makes a business decision based on the information.

This course is designed for candidate who need to work with data to create a model and performed predictive analytics. The predictive analytics can be visualized as a report or dashboard. Candidate will learn what are the six processes and build a model that will best assist the decision making.

What You'll Learn

1. Acquire the knowledge and ability to evaluate the business objectives, needs and relevance of modelling business processes Equip learners with a basic appreciation of data analytics.
2. Acquire the knowledge and ability to discover first insights into the data. Use of appropriate tools to store data.
3. Acquire the knowledge and ability to uncover interesting subsets of data to form the hypothesis regarding hidden information.
4. Acquire the knowledge and ability to transform and clean the data for data modelling and visualization.

Entry Requirements

Open to all unemployed or employed Singaporeans and Singapore Permanent Residents who are looking for training opportunities to transit to a new sector or job role. Applicants must be committed to completing training and passing assessments.

Applicants should:
- Possess a positive attitude and apply the new skill they have gained back in their workplace
- Have at least 3 years of working experience in relevant roles
- Have obtained a diploma and above academic qualification
- Be able to operate a computer or laptop to perform basic tasks
- Applicants who do not meet these recommended requirements may be considered on a case-by-case basis

Course Details

Duration 12 hours
Language English
Training Commitment Part Time
Total Enrolled 138 students
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Note: To apply for this course, visit the SkillsFuture website or contact the training provider directly.

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