PPW
Research #15
Data & Analytics
Data & Analytics
Data Science Fundamentals for Business
Understand the end-to-end data-science workflow and use data, models and evidence to answer meaningful business questions.
Course overview
Turn learning into workplace capability.
Understand the end-to-end data-science workflow and use data, models and evidence to answer meaningful business questions. Participants work through realistic workplace scenarios, guided practice and structured reflection so they can apply the learning immediately.
The programme connects analytical thinking with data quality, reproducible analysis, clear visual communication, and decisions that can be supported by evidence.
By the end
Learning outcomes
- Frame a business question as a data-science problem.
- Prepare data and perform exploratory analysis.
- Explain common modelling approaches and evaluate results.
- Communicate findings responsibly with clear limitations.
By the end
Learning objectives
- Explain data science lifecycle and connect it to the participant's workplace context.
- Apply problem framing and data sources using a structured method and appropriate supporting evidence.
- Use data preparation to complete a realistic task accurately and efficiently.
- Evaluate exploratory analysis and visualisation by applying quality criteria, controls, and professional judgement.
- Integrate modelling and evaluation with related tools, people, information, and review points.
- Produce a practical, workplace-ready output together with a clear next-step implementation plan.
Practical curriculum
Course outline: Data Science Fundamentals for Business
A focused sequence designed for application, reflection and measurable next steps. Expand each module for its detail and practical exercise.
01. Data Science Lifecycle
- Business question, analytical purpose, measures, assumptions, and success criteria for data science lifecycle.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying data science lifecycle.
02. Problem Framing and Data Sources
- Business question, analytical purpose, measures, assumptions, and success criteria for problem framing and data sources.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying problem framing and data sources.
03. Data Preparation
- Business question, analytical purpose, measures, assumptions, and success criteria for data preparation.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying data preparation.
04. Exploratory Analysis and Visualisation
- Business question, analytical purpose, measures, assumptions, and success criteria for exploratory analysis and visualisation.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying exploratory analysis and visualisation.
05. Modelling and Evaluation
- Business question, analytical purpose, measures, assumptions, and success criteria for modelling and evaluation.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying modelling and evaluation.
06. Storytelling, Ethics and Next Steps
- Business question, analytical purpose, measures, assumptions, and success criteria for storytelling, ethics and next steps.
- Required data, structure, quality checks, preparation, and reproducibility considerations.
- Appropriate analytical or visual methods and interpretation of the resulting evidence.
- Limitations, misleading conclusions, governance concerns, and communication of recommended action.
Practical exercise: Participants analyse a realistic sample dataset, validate the result, and communicate one defensible recommendation while applying storytelling, ethics and next steps.
Programme detail
How the course runs
Methodology
- Short instructor explanations that establish the principles, terminology, and workflow of Data Science Fundamentals for Business.
- Live demonstrations and worked examples and hands-on analysis of a realistic sample dataset.
- Individual, pair, and group activities using realistic inputs that do not require disclosure of confidential information.
- Facilitated review, peer discussion, trainer feedback, and correction against clear quality criteria.
- A final application task plus a personal or team action plan for transfer to the workplace.
Assessment & evidence of learning
- Opening diagnostic questions to establish experience, needs, and relevant workplace scenarios.
- Observation of guided practice and completion of module activities relevant to Data & Analytics.
- Knowledge checks, review questions, or scenario decisions at appropriate points in the programme.
- A final practical output, simulation, presentation, analysis, or implementation plan reviewed against stated criteria.
- Trainer feedback and participant reflection identifying strengths, corrections, and next-step workplace actions.
Prerequisites
- No formal prerequisite is required; relevant workplace experience will help participants contextualise the activities.
Course completion & workplace transfer
- Record corrections, open questions, support needs, and an accountable next action for workplace application.
- Review the final practical output against the learning outcomes and complete the trainer's closing knowledge check, reflection, or skills demonstration.
Who should attend
Who this course is for
Analysts, managers, technical professionals and teams beginning data-science initiatives.
01
Live online via Zoom Meeting
Join a scheduled public intake from anywhere. Trainer-led, camera-on, with hands-on exercises and Q&A throughout.
02
Private in-house group session on Zoom
The same programme run privately for one organisation on its own date, with examples tailored to your policies and roles.
03
Joining details by email
The Zoom link, meeting ID and passcode are sent automatically once payment succeeds, with reminders one day and one hour before the start.
Course questions
What organisations usually ask
Is Data Science Fundamentals for Business HRD Corp claimable?
The programme can be proposed as HRD Corp claimable training, subject to the employer’s eligibility, current scheme rules, course registration and final approval.
Who should attend this course?
Analysts, managers, technical professionals and teams beginning data-science initiatives.
How long is the course and how is it delivered?
The standard duration is 3 days. Every session runs live on Zoom with a trainer — there is no face-to-face or physical classroom option. Available formats: Live online via Zoom Meeting, Private in-house group session on Zoom.
How do I receive the Zoom link?
Reserve a seat online and complete payment. The Zoom joining link, meeting ID and passcode are emailed automatically the moment payment succeeds, followed by an automatic reminder one day before and one hour before the session starts.
What do I need to join the session?
A computer with a stable internet connection, the free Zoom client, a webcam and a headset. Sessions run in Malaysia time (GMT+8). Software used in hands-on courses should be installed before day one — the joining email lists what to prepare.
Build a stronger team
Bring Data Science Fundamentals for Business to your organisation.
Tell us your preferred dates, team size and learning priorities. We will prepare a tailored programme and quotation.
*HRD Corp claimability is subject to employer eligibility, current scheme rules, course registration and approval. Public fees are indicative until confirmed in writing. Certification, licensing and statutory pathways may require an approved provider, separate assessment, experience or registration set by the relevant authority or awarding body.