Drug development: Analyze clinical trial
data to evaluate the efficacy and safety of drugs. Environmental science: Climate change: Analyze meteorological data, study climate change trends, and predict extreme weather events. Astronomy: Space exploration: Analyze astronomical observation data to study the origin and evolution of the universe. 3. Social science Urban planning: Traffic flow analysis: Analyze traffic flow data, optimize traffic light settings, and alleviate traffic congestion.Public policy: Crime analysis: Analyze crime data Email List identify crime hotspots, and formulate corresponding preventive measures. 4. Other fields Sports: Sports performance analysis: Analyze athletes' sports data to improve training results. Education: Student learning effect analysis: Analyze student performance data, understand students' learning situation, and improve teaching methods. General process of data analysis No matter in which field, data analysis usually follows the following steps: Propose a question: Clarify the problem to be solved.
https://lh7-rt.googleusercontent.com/docsz/AD_4nXdayjd0oMW7ChDodVGlZdI4oYLY3URCgx1OZbr4eCNfBHDAJ3Wpfq5wfoIzQuSYlTPa925T7HeGlrfAWwH1Qpgmp50aJiFQ-tpsNkTk3SPyQvg2oMhJ-knnodQkWvoPHhyZZxbyCFaXjtXs8dHvD_KBUCT3?key=WXcvWVK6s82QBl2SmcW8IQ
Collect data: Collect relevant data from various sources. Data cleaning: Clean and preprocess the data to remove errors and missing values. Data exploration: Explore data features through visualization and other means. Modeling analysis: Select a suitable model for analysis and draw conclusions. Result presentation: Present the analysis results in the form of charts or reports. Data analysis tools Commonly used data analysis tools include: Excel: Simple and easy to use, suitable for the analysis of small data sets.
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