Data-driven storytelling - You will learn how to turn numbers and charts into a clear narrative, making your insights easier to explain, better tailored to your audience, and more persuasive in presentations.
Clear, polished slides - You will discover how to design charts and slides that are clean, visually appealing, and focused on key messages, so your audience sees what matters without unnecessary clutter.
Smarter chart selection - You will learn how to match chart types to your goal and data structure, so you can present comparisons, trends, shares, and relationships in a way people quickly understand.
Hands-on tool practice - You will practice building effective visuals in Excel, PowerPoint, and tools like Datawrapper and Flourish, so you can apply the techniques immediately in your everyday work.
Stronger live presenting - You will learn how to select the right information and structure a live data narrative, so you can explain results clearly, hold attention, and strengthen your message with analogies.
Avoiding misleading visuals - You will understand the principles of ethical data visualization, helping you avoid deceptive scales, poor chart choices, and unintentional distortion of findings in reports and slides.
A wider visual toolkit - You will go beyond basic bar, line, and pie charts and explore maps and less standard formats, giving you more ways to present different data types and business context effectively.
AI support for storytelling - You will see how AI can support your work on visuals and narrative, from organizing ideas to speeding up preparation and helping you frame sharper, more compelling conclusions.
Data storytelling connects analysis with the way people understand and remember information. Participants examine the role of narrative in presenting data, review examples of strong data-based stories, and learn how to adapt a message to the audience, context and situation.
Clear visualizations rely on disciplined choices in color, typography and content hierarchy, not only on technical chart creation. Participants examine how to direct attention to key conclusions, maintain visual consistency, and avoid misleading design decisions that distort interpretation.
Choosing the right chart depends on what the data should explain and how the audience is expected to read it. The section compares bar, line and pie charts, then extends the discussion to maps and less standard formats through examples built on different data sets.
Using Excel, PowerPoint and other everyday tools, participants practice building charts that communicate clearly in common business settings. The section also introduces free platforms such as Datawrapper and Flourish, with attention to when these tools fit specific visualization tasks.
Presenting data live requires a different approach than building reports or dashboards for later use. Participants work on selecting only the information that supports the message, shaping a convincing narrative, and using metaphors, analogies and speaking techniques to hold attention.
AI can support data storytelling by accelerating idea generation, helping frame insights and suggesting ways to present information more clearly. This part focuses on where such assistance is useful in visualization work and how to use it to strengthen, rather than replace, analytical judgment.
Hands-on work accompanies every stage of the training, so participants immediately apply each concept to realistic communication tasks. Exercises reinforce decisions about message, chart choice and delivery, making it easier to connect principles with everyday reporting and presentation practice.
Basic data literacy - You should be comfortable reading tables, understanding basic metrics, and drawing simple conclusions from data, so you can focus on presentation rather than analysis basics.
Excel or PowerPoint basics - You should know how to handle basic tasks in Excel or PowerPoint, such as editing data, working with slides, and creating simple charts based on your own materials.
Experience presenting results - It is helpful if you already have some experience presenting results or reports, because the training develops how you tell data stories rather than teaching public speaking from scratch.
Readiness for hands-on work - You should be ready to work actively with examples and exercises, test different visualization approaches, and improve your own materials during the sessions and between modules.
