NICF – Data Visualisation (Blended Learning)

NICF -  Data Visualisation (Blended Learning) is a vendor-agnostic and software product, agnostic with learners gaining the knowledge, skills, and abilities in data visualisation in contrast to the other courses in the market than gaining knowledge, skills and abilities in a software vendor dependant product functions and features. 

NICF – Data Visualisation (Blended Learning)

Key Facts 

  • Course duration: Please refer to the details below

  • Intake dates: Start in January, April, July, or October

  • Total training fee: S$1,950

  • Funding: Up to 90% of funding is available. Please see the criteria below
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This module helps students to join the dots between mathematics, programming, and business analysis. This module provides an introduction as well as hands-on experience in data visualization, visual analytics, and visual data storytelling. If learners have no prior knowledge or learners with basic math skills but want to apply them in data science or learners have good programming skills but lack math, this module bridges the gap between mathematics and programming. It introduces design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making in the field of business analytics. Students will know how to structure and streamline data analysis assignments; collect, prepare, and “wrangle” data, and highlight implications using visualizations and storytelling.

 

 

Please choose one of the following options:

1

4.5 days, 9 AM - 6 PM (Monday - Friday). 9 hours/day *4 days + 3 hours of Assessment.

2

2 weekends (Saturday and Sunday) + 3 hours of Assessment. 

3

14.5 days - 2 weeks, 7 PM - 10 PM (Monday - Friday). 

4

Customized upon corporate request. 

5

3 months, 1 session/week, 3 hours/session.

At the end of the course, learners will be able to:

1) Explain data visualization software and techniques

2) Appraise techniques for visualizing trends and patterns

3) Analyse data using storytelling methods

4) Evaluate data stories and creating frameworks

5) Explain filtering and outline techniques to organize data

The assumed skills and knowledge for this course are as follows:

  • Aged between 21 years old and above;
  • Minimum at least Learners must have ONE GCE O Level;
  • Able to read, listen and speak English at a proficiency level equivalent to Employability Skills System (ESS) Literacy Level 6;
  • Able to count with a proficiency equivalent to Employability Skills System (ESS) Numeracy Level 6;
  • Have basic computer literacy Level 2.

This accreditation enables Singaporeans and Singapore PR holders to get a part of their course funded by the SSG. Subjected to the eligibility and funding caps, the funding support is up to 90% for Singaporeans and up to 80% for Singapore PR holders.

Course Fee Funding for Self-sponsored Individuals (as of 30 Apr 2021)

Certifiable courses approved by SSG Course level Courses starting before 1 Jan 2022 Courses starting on or after 1 Jan 2022
Singapore Citizens (SCs) and Permanent Residents (PRs) (Self-sponsored individuals must be at least 21 years old) PMET Up to 50% of course fees, capped at $15 per hour Up to 50% of course fees
Non-PMET Up to 80% of course fees, capped at $17 per hour
SCs aged ≥ 40 years old (SkillsFuture Mid-career Enhanced Subsidy) PMET Up to 90% of course fees, capped at $50 per hour Up to 70% of course fees
Non-PMET Up to 90% of course fees, capped at $25 per hour

To receive funding from SSG, you must meet the following criteria:

  • Pass the course
  • Achieve a minimum of 75% of the attendance
  • Must not be barred from receiving government grants

  • You may also be eligible for the course fee (CF) funding at the current rates offered by SkillsFuture Singapore (SSG). For more details, please refer to this page.
  • Individuals aged 40 and above may also be eligible for the SkillsFuture Mid-Career Enhanced Subsidy (MCES). For more details, please refer to this page.
  • Singaporeans may also use their SkillsFuture credit to offset course fees payable. For more details, please refer to this page.

Modules

Learners would be able to use new data visualisation tools and techniques and introduce new or emerging visualisation tools and techniques that are fit for purpose.

Learners would be able to use a range of methods to portray data patterns, trends, and correlations, and exercise judgment on the presentation of data to ensure that critical trends and findings are presented in an optimal way.

Learners would be able to understand dashboard development processes and techniques and develop dashboards and scorecards incorporating advanced visualization techniques and embedding analytics capabilities.

Learners would be able to use features of data displays and align interpretation and presentation of data analytics findings with subject matter experts.

Learners would be able to use strategic visualization and mapping techniques, review tables, graphs, and dynamic data displays, to ensure key questions from key stakeholders are addressed, and design features of data displays including navigation, layout, user interface, and user experience of interactive graphics.

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Contact a programme advisor by calling
+65 6580 7700

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