NICF - Statistical Analysis (Blended Learning)

Statistics are applied every day, in research, industry, and government, to become more scientific about decisions that need to be made. Statistical Analysis is "the science of collecting, exploring, and presenting large amounts of data to discover underlying patterns and trends. Thus, many organisations, in their effort to organize data and predict future trends based on information, rely on statistical analysis. Learners will be introduced to statistical data analysis and competent in applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and non-parametric statistics.

NICF - Statistical Analysis (Blended Learning) is a vendor-agnostic and software product, agnostic with learners gaining the knowledge, skills, and abilities in applying statistics for business and data analysis in contrast to the other courses in the market than gaining knowledge, skills, and abilities in a software vendor dependant product functions and features. This modular transfers the knowledge, skills, and abilities related to statistical analytics.

NICF – Statistical Analysis (Blended Learning)

Key Facts 

  • Course duration: 3 months (part-time), 1 day/week, 1 hour/day

  • Intake dates: Start in January, April, July, or October
  • Total training fee: S$1,950

Statistical Analysis is the science and the art of learning from data. As a study, it is concerned with the collection, analysis, and interpretation of data, as well as the effective communication and presentation of results relying on data. This program in Statistical Analysis provides you with the necessary tools and conceptual foundations in quantitative reasoning to extract information intelligently from this sea of data. This is essential for the following industries. The training will provide the learners the skills to summarise data using business performance reports and develop business performance dashboards.

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

1) Explain statistical methods and applications.

2) Appraise descriptive analysis to understand relationships.

3) Analyse relationships using statistical modelling techniques.

4) Evaluate model effectiveness.

5) Explain optimization and trend analysis.

  • 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 Learners may be required to submit the relevant documents conforming to the pre-requisites for the course as stated above, where necessary

Learners may be required to submit the relevant documents conforming to the pre-requisites for the course as stated above, where necessary if they do not the pre-requisites.

Modules

Explain statistical methods and applications. Evaluate prospective analytical tools and platforms for their functional capabilities and ability to meet the requirements of the analytic environment. Features and applicability of various data models.

Appraise descriptive analysis to understand relationships. Develop mathematical models to isolate trends and optimise data-driven decision-making. Range of statistical and advanced computational modelling techniques. Features, pros, and cons of various statistical approaches, algorithms, and tools.

Analyse relationships using statistical modelling techniques. Develop regression, models, including linear, multiple and logistic, regression models. Apply complex and advanced statistical analysis and modelling techniques. Uncover underlying relationships among different variables. Elements of various algorithms.

Evaluate model effectiveness. Develop testing procedures to evaluate the data model. Testing procedures to evaluate statistical models.

Explain optimization and trend analysis. Develop new algorithms to enable the learning, improvement, adaptation, or reproduction of outcomes. Create learning models with a discrete set of environment states, actions, and reinforcement signals. Analyse root causes of any issues highlighted. Facilitate changes to statistical models, to optimise performance and yield intended outcomes. Impact of changes to algorithms and models on performance outcomes. Advanced mathematical models and theories.

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+65 6580 7700

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