AI and Data in Business
Statistics for Business Decision-Making
This practical course develops directly applicable capability in Statistics for Business Decision-Making. Participants work in depth on Statistical Thinking for Business, and Describing Data, and Sampling and Estimation, then convert the methods into tools and actions suited to their workplace.
Objectives
- Apply the principles and methods of statistical thinking for business in a workplace context.
- Apply the principles and methods of describing data in a workplace context.
- Apply the principles and methods of sampling and estimation in a workplace context.
- Apply the principles and methods of testing business differences in a workplace context.
- Apply the principles and methods of relationships and prediction in a workplace context.
- Apply the principles and methods of evidence-based decision case in a workplace context.
Target audience
- Professionals responsible for the subject area
- Managers and supervisors
- Analysts, coordinators, and specialists
- Project and improvement teams
- Employees preparing for broader responsibilities
Program outline
A clear structure for the learning journey.
Outline points are grouped in one designed block instead of being treated as separate module cards.
01Statistical Thinking for Business3 topics
- Populations, samples, variables, and observations
- Variation and uncertainty in business processes
- Descriptive versus inferential questions
02Describing Data3 topics
- Mean, median, percentiles, and spread
- Distributions, skew, and outliers
- Choosing summaries that fit the data
03Sampling and Estimation3 topics
- Random, stratified, and biased samples
- Sampling error and confidence intervals
- Interpreting margin of error correctly
04Testing Business Differences3 topics
- Null and alternative hypotheses
- Practical versus statistical significance
- Errors, power, and sample-size considerations
05Relationships and Prediction3 topics
- Correlation and confounding
- Simple regression interpretation
- Avoiding extrapolation and causal overstatement
06Evidence-Based Decision Case3 topics
- Selecting a suitable statistical approach
- Checking assumptions and data limitations
- Communicating the result and decision risk
Materials provided
- ○ Course-specific presentation slides
- ○ Practical exercises and facilitated activities
- ○ Course-specific worksheets, checklists, and templates
- ○ Applied workplace case studies
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D adapts this program to the participant group and workplace context. Delivery combines structured explanation with course-specific exercises, realistic cases, working tools, and an action-planning component so participants can transfer the learning to their roles.
Related courses
AI Agents and Workflow Automation for Business Operations
This course helps business and operations teams understand how AI agents and workflow automation can reduce repetitive work, improve handoffs, and support faster execution. Participants learn how to map processes, identify automation candidates, design human-in-the-loop controls, and manage risks before scaling AI-enabled workflows.
View courseAI Change Management and Adoption for Managers
This course helps managers lead teams through AI adoption with clarity, confidence, and responsible use. Participants learn how to address resistance, redesign work, set expectations, coach employees, define safe-use rules, and measure adoption without creating fear or unrealistic expectations.
View courseAI for Business Leaders and Department Managers
This course helps business leaders and department managers understand how artificial intelligence can be used responsibly across departments. Participants explore practical AI use cases, productivity opportunities, governance requirements, implementation risks, and decision-making considerations without needing a technical background.
View course