“Citizen Data Science” refers to a concept where individuals without formal data science training or job titles in data science actively engage in data analysis and related activities. These individuals, often called “citizen data scientists,” use accessible tools and resources to work with data and derive insights without relying solely on data science professionals. Here are key aspects of citizen data science:

1. Accessibility: Citizen data science aims to make data analysis and insights accessible to non-experts. It encourages individuals from various backgrounds, such as business, healthcare, marketing, and education, to use data to inform decision-making.

2. Tools and Platforms: Citizen data scientists rely on user-friendly tools and platforms that don’t require advanced coding or statistical expertise. These may include data visualization tools, drag-and-drop analytics platforms, and business intelligence (BI) software.

3. Data Preparation: Data cleaning, transformation, and preparation are crucial steps in data analysis. Citizen data scientists often use tools with built-in data preparation capabilities to simplify these tasks.

4. Data Exploration: Citizen data scientists explore data through visualizations, dashboards, and interactive reports. They can gain insights into trends, patterns, and anomalies without delving deeply into complex statistical analysis.

5. Machine Learning and Predictive Analytics: While not as advanced as professional data scientists, citizen data scientists may use machine learning tools and automated predictive analytics to create models for simple tasks, such as forecasting or classification.

6. Domain Expertise: Citizen data scientists typically have domain expertise in their respective fields, which helps them interpret data in a meaningful context and make informed decisions.

7. Collaboration: Collaboration between citizen data scientists and professional data scientists or data analysts is encouraged in organizations. Professional data scientists can provide guidance, validate analyses, and handle more complex tasks.

8. Ethical Considerations: Like professional data scientists, citizen data scientists should be aware of ethical considerations when working with data, including privacy and bias issues.