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Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and dataengineers, and determining appropriate key performance indicator (KPI) metrics.
Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, supportvectormachines, and neural networks gained popularity.
NaturalLanguageProcessing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Scala is worth knowing if youre looking to branch into dataengineering and working with big data more as its helpful for scaling applications.
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