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Data Visualization & Statistical Techniques for Effective Decision Making

Dates Venues Register
20/09/2026 - 24/09/2026 BERLIN

Introduction

Data Visualization & Statistical Techniques for Effective Decision Making

 

 

Course Objectives

By the end of this training course, participants will be able to:

  • Apply essential statistical techniques for data analysis and validation
  • Conduct exploratory data analysis (EDA) to identify trends, patterns, and anomalies
  • Select and interpret appropriate statistical tests with confidence
  • Design clear, engaging, and purpose-driven data visualizations
  • Build interactive dashboards using Excel and Power BI tools
  • Create compelling data stories that align with business objectives
  • Translate analytical findings into strategic and actionable decisions

Who Should Attend?

This training course is particularly suitable for:

  • Data analysts, reporting specialists, and business intelligence professionals
  • Team leaders, managers, and decision-makers using data insights
  • Professionals in finance, marketing, supply chain, and operations
  • Individuals responsible for dashboards, reports, and executive presentations
  • Anyone aiming to improve data-driven decision making skills

 

Course Content

Day1

Day One: Foundations of Data Analysis & Descriptive Statistics

  • The strategic importance of data analysis in modern business
  • Understanding data types: categorical vs. numerical, continuous vs. discrete
  • Measures of central tendency: mean, median, mode — when and why to use each
  • Measures of dispersion: range, variance, standard deviation, coefficient of variation
  • Introduction to the normal distribution and why it matters
  • Practical exercises using Excel for calculating and summarizing data
Day2

Day Two: Exploring Data & Basic Inferential Statistics

  • Introduction to exploratory data analysis (EDA) – objectives and benefits
  • Visual tools for EDA: histograms, boxplots, scatterplots
  • Identifying outliers and data anomalies
  • Correlation analysis: understanding relationships between variables
  • Basics of inferential statistics: confidence intervals, hypothesis testing (t-tests, chi-square)
  • Hands-on labs using sample datasets
Day3

Day Three: Data Visualization Principles & Best Practices

  • Why visuals matter: cognition and data communication
  • Principles of effective data visualization: simplicity, clarity, focus
  • Choosing the right visual: comparisons, relationships, distributions, compositions
  • Avoiding common pitfalls: misleading scales, clutter, cherry-picking data
  • Building professional visuals in Excel (charts, pivot charts) and customizing for impact
  • Storyboarding a data presentation
Day4

Day Four: Advanced Visualizations & Interactive Dashboards

  • Multi-variable charts: combo charts, bubble plots, heatmaps
  • Introduction to Power BI (or optional Tableau overview)
  • Designing interactive dashboards: slicers, drill-downs, trend lines
  • Bringing data from multiple sources: Excel + external files in Power BI
  • Hands-on exercise: creating a dynamic sales performance dashboard
Day5

Day Five: From Analysis to Data Storytelling & Presentation

  • Principles of data storytelling: structure, context, audience focus
  • Building a compelling narrative with data: what story are you telling?
  • Workshop: participants develop a mini-project using their chosen dataset
  • Group presentations with peer & instructor feedback
  • Developing a personal action plan: how to apply these tools and techniques in your role

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