Effective Business Decisions Using Data Analysis
Course Overview
Participants will learn how to interpret analytical findings correctly, integrate quantitative reasoning into managerial processes, and confidently utilize evidence-based insights to drive informed decisions.
Course Objectives
By the end of this training course, participants will be able to:
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Understand and appreciate data analytics as a key decision-support tool.
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Explain the scope, structure, and methodology of data analytics.
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Apply a range of data analytical techniques to real-world management challenges.
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Interpret and critically assess statistical evidence for decision-making.
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Identify relevant areas within their organization where data analytics can be effectively applied.
Course Audience
This training course is designed for professionals who rely on data-driven insights in their decision-making roles, including:
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Managers and decision-makers seeking to enhance analytical capabilities
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Professionals in management support or planning roles
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Business analysts and professionals working regularly with data
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Individuals aiming to apply evidence-based reasoning to operational or strategic decisions
Course Methodology
Participants will engage in:
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Instructor-led presentations and guided exercises
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Group discussions and collaborative problem-solving
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Practical workshops using Microsoft Excel for real data analytics
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Analysis of real-world datasets from participants’ own work environments
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Emphasis on valid interpretation of statistical results within a management context
Course Outline
Day 1: Setting the Statistical Scene in Management
- The quantitative landscape in management decision-making
- Thinking statistically: identifying and using KPIs effectively
- Integrative elements of data analytics
- Understanding data types, quality, and preparation
- Exploratory data analysis using Excel (Pivot Tables)
- Using summary tables and visual displays to profile sample data
Day 2: Evidence-Based Observational Decision Making
- Numeric descriptors to profile sample data
- Central and non-central location measures
- Quantifying variability and dispersion in data
- Examining data distributions (skewness and bimodal analysis)
- Exploring relationships between numeric measures
- Performing breakdown analysis for decision insights
Day 3: Statistical Decision Making – Drawing Inferences from Data
- The foundations of statistical inference
- Quantifying uncertainty using the normal probability distribution
- The importance of sampling and its implications
- Random-based sampling techniques
- Understanding the sampling distribution concept
- Confidence interval estimation and interpretation
Day 4: Statistical Decision Making – Hypothesis Testing
- The rationale and process of hypothesis testing
- Understanding and avoiding Type I and Type II errors
- Single population hypothesis testing (mean-based tests)
- Two independent population mean comparisons
- Matched-pairs test scenarios
- Comparing means across multiple populations (ANOVA overview)
Day 5: Predictive Decision Making – Statistical Modeling and Data Mining
- Building predictive models using statistical relationships
- Regression analysis for management forecasting
- The regression model-building process: evaluation and validation
- Introduction to data mining: concepts and evolution
- Descriptive data mining and managerial applications
- Predictive (goal-directed) data mining for strategic decision-making
Certificates