| Primary Focus | Building and maintaining software applications | Extracting insights and knowledge from data |
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| Goal | Delivering functional, reliable, and efficient software | Extracting meaningful patterns and information from data |
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| Key Activities | Coding, testing, debugging, and maintaining code | Data cleaning, analysis, modeling, and interpretation |
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| Tools and Languages | Programming languages (e.g., Java, Python, C++) | Programming languages (e.g., Python, R), SQL, and tools for data analysis (e.g., Pandas, NumPy) |
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| Development Process | Follows software development life cycle (SDLC) | Often follows the data science life cycle (DSLC) |
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| Outcome | Software applications, websites, systems | Insights, predictions, recommendations from data |
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| Key Skills | Programming, problem-solving, software design | Statistics, machine learning, data analysis, domain knowledge |
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| Metrics and Testing | Reliability, performance, usability, security | Model accuracy, precision, recall, AUC, F1 score |
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| Iteration | Agile methodologies often used for iterative development | Iterative exploration and refinement of data models |
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| Domain Focus | Wide range of domains (e.g., finance, healthcare, gaming) | Various domains (e.g., finance, healthcare, marketing) |
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| Example Tasks | Building a mobile app, web development | Predictive modeling, clustering, classification |
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| Data Handling | Typically involves managing input/output data within the application | Involves cleaning, transforming, and analyzing large datasets |
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| Tools and Frameworks | Integrated Development Environments (IDEs), version control (e.g., Git) | Jupyter Notebooks, TensorFlow, PyTorch, scikit-learn |
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