Data in Action: Starts with a compelling visual representation of data (e.g., an infographic, an interactive chart) to illustrate the power and relevance of data in everyday life.

Program Overview: A brief introduction to the learning objectives and highlight the importance of data literacy in navigating the digital world.

Defining Data Literacy: An explanation of the concept of data literacy and its key components:

1. Data Collection: Understanding how data is gathered from various sources (surveys, sensors, online activity).

2. Data Analysis: Interpreting data, identifying patterns, and drawing meaningful insights.

3. Data Communication: Presenting data effectively through visualizations and storytelling.

4. Critical Evaluation: Assessing the reliability, validity, and potential biases in data.

Why Data Literacy Matters: General discussion about the importance of data literacy in making informed decisions, understanding social issues, and participating in civic discourse.

Types of Data: An exploration of different types of data (numerical, categorical, textual) and their characteristics.

Data Sources: General discussion about various sources of data, including government statistics, scientific research, social media, and news reports.

Basic Statistical Concepts: An introduction to basic statistical concepts such as mean, median, mode, and standard deviation, and their relevance in data interpretation.

Data Visualization Tools: An overview of common data visualization tools and platforms (e.g., spreadsheets, online charting tools).

Principles of Visualization: Open discussion about the principles of effective data visualization, including clarity, accuracy, and visual appeal.

Choosing the Right Chart: An exploration of different types of charts and graphs (bar charts, line graphs, pie charts) and their suitability for different data types.

Telling a Story with Data: Emphasizes the importance of using visualizations to tell a compelling story and communicate insights effectively.

Tools and Techniques: A demonstration of the basic techniques for creating data visualizations using spreadsheets or online tools.

Data Bias: Overview discussion about how bias can be introduced in data collection, analysis, and presentation, and its potential impact on interpretation.

Misleading Visualizations: An exploration of how visualizations can be manipulated to mislead or misrepresent data.

Data Privacy and Security: Emphasizes the importance of responsible data handling and protecting sensitive information.

Recap: Summarizes key takeaways from the module.

Q&A: Time allowed for questions and discussions.

Data Exploration: Encourages participants to explore data sources, create their own visualizations, and engage in critical data analysis.