Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves working with raw data to extract meaningful insights that can inform actions and improve outcomes in various fields such as business, science, and social sciences

. Key aspects of data analysis include:

• Data Collection: Gathering relevant data from various sources.
• Data Cleaning: Removing errors, duplicates, and irrelevant information to ensure data quality.
• Data Transformation and Modeling: Converting data into suitable formats and applying statistical or computational models to identify patterns or test hypotheses.
• Data Interpretation: Making sense of the analyzed data to draw conclusions.
• Data Visualization: Presenting data graphically (charts, graphs, maps) to facilitate understanding and communication of insights

Data analysis can be descriptive (summarizing data), exploratory (discovering new patterns), confirmatory (testing hypotheses), predictive (forecasting future trends), or text analytics (analyzing unstructured text data)

. It is widely used to make informed decisions, optimize processes, and guide research or business strategies. For example, businesses use data analysis to understand customer behavior and improve marketing, while healthcare providers analyze data to enhance patient care and predict disease outbreaks

. Data analysis can be quantitative (focused on numerical data and statistical methods), qualitative (focused on textual or non-numerical data and thematic interpretation), or mixed methods combining both approaches

. In summary, data analysis is a critical practice of turning raw data into actionable knowledge through systematic examination and interpretation