Data and Visualization - Data Science

Data is a collection of values or information that can be processed to generate knowledge or insights. In the context of data science, data can take the form of numbers, text, images, sounds, or a combination of these forms. Data serves as the fundamental basis for analysis and modeling in data science, providing the information needed to make decisions, predict trends, and understand phenomena.

Definition of Data

Quantitative Data: Data that is measured and expressed in numbers, such as height, weight, temperature, and quantity.
Qualitative Data: Data that is descriptive and not expressed in numbers, such as color, gender, or categories.

How to Collect Data

1. Direct Observation: Observing objects or phenomena directly to gather data. Example: Counting the number of people entering a store over a day.
   
2. Experimentation: Collecting data through experiments designed to test specific hypotheses. Example: Testing the effectiveness of a new drug with a control group and an experimental group.
   
3. Surveys and Questionnaires: Collecting data by asking people to provide information through structured questions. Example: Conducting a customer satisfaction survey for a product or service.
   

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the context of data science and analytics, data visualization is a critical step that follows data collection and analysis, as it helps to communicate insights clearly and efficiently.

Charts and Graphs:
Bar Chart: Compares quantities across different categories.
Line Graph: Shows trends over time.
Pie Chart: Represents parts of a whole.

Fig 1. Pie chart represents the egg yolk composition