Additional information
| Course Duration | |
|---|---|
| Training Location | |
| Delivery Format |
- From: Mar 09, 2027
- To: Mar 10, 2027
-
Duration:
2 Days
- Location:
Course Code: AX-DAT-003
Data Analysis Techniques
Analysis Methods and Evidence-Based Decision-Making Using Microsoft Excel
$1,250.00
Course Information
Course Overview
In today’s data-driven business environment, effective decision-making depends increasingly on the ability to transform data into reliable, actionable evidence. Organisations generate and collect vast amounts of numerical information, but its value depends on how accurately it can be analysed, interpreted, and communicated.
This Data Analysis Techniques training course provides participants with the practical knowledge and analytical skills required to transform numerical data into meaningful information and support robust, evidence-based business decisions.
Learning Objectives
- Apply structured approaches to analysing numerical data
- Select appropriate analytical techniques for different business problems
- Use descriptive and statistical methods to interpret datasets
- Identify potential sources of analytical error, bias, and misleading conclusions
- Analyse trends and time-dependent data
- Perform scenario and sensitivity analysis
- Apply correlation and regression techniques to investigate relationships and support prediction
- Use Excel tools such as PivotTables, PivotCharts, Solver, and statistical functions
- Apply ANOVA and other statistical methods to support evidence-based conclusions
- Transform numerical data into meaningful evidence for business decision-making
Who Should Attend
- Performance Management and Monitoring
- Planning and Forecasting
- Data Analysis
- Management and Leadership
- Finance and Accounting
- Human Resources
- Quality Management and Control
- Engineering and Technology
- Operations and Business Improvement
- Any function where numerical data is used to support decisions
Course Outline
This course covers the following core modules:
- Logical and Reliable Data Analysis, Descriptive Statistics and Pivot Tables
- Data Mode Shape and Time-Series Analysis
- Scenario Analysis and Interactive Spreadsheets
- Regression Analysis and Correlation
- Data-Driven Methods and Analysis of Variance
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