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About Dataanalysis
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information and support decision-making. It converts raw information into meaningful insights that help businesses, researchers, and organizations act with evidence rather than relying on gut instinct. The field spans techniques from basic reporting to advanced methods like predictive modeling and machine learning, and is widely applied in business, healthcare, and artificial intelligence.
The process typically follows a systematic workflow that includes collecting data, cleaning it for accuracy, exploring patterns, analyzing results, and sharing findings. Analysts often categorize work into four types: descriptive analytics for what happened, diagnostic for why it happened, predictive for what is likely next, and prescriptive for what actions to take. Key skills include SQL, Excel, statistics, and tools such as Python or Power BI, as outlined in resources like the Ajay Yadav data analyst cheat sheet.
Qualitative and quantitative research represent two main approaches within data analysis. Quantitative research uses measurable, numerical data for statistical analysis, while qualitative research focuses on non-numerical data like interviews and observations to identify patterns and themes. Events such as Data Quest UPC 2025 at Universidad Peruana de Ciencias Aplicadas showcase students applying these methods using tools including RStudio and Python.