Data Science Process
Data Science Process is a systematic approach to extract insights and knowledge from data. It involves a series of steps that includes data collection, data cleaning, data exploration, data analysis, model building, model evaluation, and deployment. The process starts with identifying the problem and defining the research question. Then, data is collected from various sources and preprocessed to remove any inconsistencies and errors. Exploratory data analysis is performed to understand the data and identify patterns and trends. Based on the insights gained, models are built using machine learning algorithms. The models are then evaluated using various metrics to determine their accuracy and effectiveness. Finally, the models are deployed in a production environment to make predictions and generate insights.
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- Applications: Applications refer to the practical use of data science and artificial intelligence techniques to solve real-world problems. These can range from simple tasks such as predicting customer churn for a business to complex problems such as deve ... Read More >>
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