The Impact of AI and Automation
The most significant change in the data science field is the growing role of AI-driven automation. Tools and platforms can now manage many routine and repetitive tasks that used to define entry-level data science jobs. These tasks include.
Data cleaning and preparation: AI can automate the process of sorting through messy datasets, spotting and fixing errors, and getting data ready for analysis.
Preliminary analysis and modeling: Automated machine learning (AutoML) platforms can build and refine models with little human involvement, making it easier for non-specialists to carry out basic predictive analytics.
Report generation: Generative AI can produce reports and visualizations, automating the sharing of simple insights.
This automation indicates that data scientists are spending less time on tedious, low-level tasks. As a result, competition for entry-level positions is getting tougher, especially for those without specialized skills or hands-on experience.