Data cleaning, preprocessing, and basic analysis are examples of repetitious operations that automation is predicted to streamline, revolutionising data science careers. Automation will increase the efficiency of many operations, but it won't replace the need for qualified data scientists who can create, analyse, and optimise intricate models. There will always be a need for data scientists, especially those with expertise in AI, machine learning, and advanced analytics. Data strategy, insight extraction, and model innovation responsibilities will continue to be vital as businesses depend more and more on data-driven choices. Consider taking a data science course to obtain a competitive advantage in this developing field.
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