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.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: Statistical Thinking for Business
Populations, samples, variables, and observations
Variation and uncertainty in business processes
Descriptive versus inferential questions
Module 2: Describing Data
Mean, median, percentiles, and spread
Distributions, skew, and outliers
Choosing summaries that fit the data
Module 3: Sampling and Estimation
Random, stratified, and biased samples
Sampling error and confidence intervals
Interpreting margin of error correctly
Module 4: Testing Business Differences
Null and alternative hypotheses
Practical versus statistical significance
Errors, power, and sample-size considerations
Module 5: Relationships and Prediction
Correlation and confounding
Simple regression interpretation
Avoiding extrapolation and causal overstatement
Module 6: Evidence-Based Decision Case
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.
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