Artificial Intelligence

Data Visualization

Course description

The course introduces the theoretical and practical fundamentals of data visualization to support effective analysis and communication.

Main Topics

The course explores the following macro-topics:

  • Introduction to Data Visualization: definition, objectives, and context
  • Principles of visual perception and color theory
  • Working with real datasets: exploration, cleaning, and presentation
  • Ethics and responsibility in data representation
  • User needs analysis and visualization customization
  • Types of charts: when and how to use them (bar chart, line chart, scatter plot, etc.)
  • Common visualization errors and how to avoid them
  • Effective design: layout, visual hierarchy, simplicity, and clarity
  • Tool selection: overview of the most popular software and libraries
  • Practical exercise on one of the presented tools

Participant profile

The course is aimed at professionals with technical skills.

Objectives

The course aims to provide a solid understanding of the fundamental principles of data visualization, with a focus on clarity, communicative effectiveness, and accuracy in representation. Participants will learn to choose the most suitable charts based on the type of data and the message to be conveyed, avoiding common errors and ambiguities.
The course also aims to develop a sensitivity to design and visual aesthetics, integrating notions of visual perception and storytelling. Participants will learn to critically read visualizations and improve existing ones. A key objective is to make data accessible, understandable, and useful for strategic decisions. At the end of the course, participants will be able to transform raw data into effective visual narratives.

Learning outcomes

At the end of the course, participants will be able to:

  • Create effective and functional visualizations for different types of data.
  • Understand the fundamentals for creating interactive dashboards.
  • Apply design principles and visual perception in data representation.
  • Communicate complex information clearly.
  • Critically evaluate existing visualizations and improve them based on context and objectives.

Participation conditions

  • Familiarity with computer use and spreadsheets (Excel or similar).
  • Basic knowledge of data analysis (e.g., averages, percentages).
  • Programming experience is not required, but a good aptitude for using digital tools is beneficial.

Cost per participant

€300

Participants

Min 5

Resources available from PNRR funds, Mission 4 – Component 2 – Investment 2.3:

Subsidies up to 80%

Cost per company

€1500

Participants

Max 15 per company

Mode

In-person

Duration

12 hours

Level

Introductory

Trainer

CIM

At the end of each course, you will receive a certificate of participation issued by the CIM4.0 Competence Center.
As an implementing body for digital transition, CIM4.0 holds the accreditation certificate from the Piedmont Region for the provision of continuous training. Certificate No.: 1503/001

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