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Foundation Of Statistics For Data Analytics

RM4,500.00Price
This 3-day Foundation of Statistics for Data Analytics course equips participants with the statistical foundations and applied modelling skills needed to extract business insights from data. Learners will cover exploratory data analysis, hypothesis testing, clustering and decision-tree techniques, prototype algorithm development, and methods for validating predictive models. Emphasis is placed on translating statistical outputs into actionable recommendations and interactive visual storytelling.
 
Through hands-on case examples and model prototyping, participants will learn to identify meaningful patterns, build simple predictive models, and communicate findings that support business decisions. The program is ideal for aspiring data analysts, BI professionals, and managers who want a practical bridge from statistics to data-driven outcomes.

This programme is created specifically for learners who wish to develop, apply and evaluate algorithms, predictive data modelling and data visualisation to identify underlying trends and patterns in data.

Frequently Asked Questions

What is the focus of this course?

Practical application of statistical methods and basic predictive modelling (clustering, decision trees) to extract and communicate business insights.

Who should attend?

Aspiring data analysts, BI professionals, managers, and business users who want a practical grounding in statistics for analytics.

What will I be able to do after the course?

Perform EDA, run clustering and decision-tree analyses, prototype simple predictive models, validate results, and present findings via interactive visual storytelling.

Do I need prior programming skills?

No — a basic familiarity with Excel or data-handling tools is helpful, but the course focuses on statistical thinking and applied modelling rather than heavy coding.

Are there hands-on activities or projects?

Yes — the program includes case studies, prototype algorithm exercises, and data storytelling workshops to apply learned techniques.

What types of modelling techniques are covered?

Exploratory data analysis, hypothesis testing, clustering algorithms, decision trees, algorithm comparison and model prototyping.

Is this course HRDC claimable?

Yes — AjarAble’s courses are generally HRDC claimable

Can the course be customized for my organization?

Yes. The modules (examples, datasets, case studies) can be tailored to industry context, internal KPIs, or specific business questions.


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