How do the job responsibilities and requirements differ between an ETL role and a Data Science role, and how can one bridge this gap effectively?

Question

How do ETL and Data Science roles vary in responsibilities and requirements, and how can one effectively bridge the gap?

Answer 1

ETL roles primarily focus on data extraction, transformation, and loading, while Data Science roles involve advanced data analysis and modeling to derive insights and predictions. To bridge the gap, professionals can build expertise in statistics, machine learning, and data analytics through relevant courses, gain experience in working with more complex data, and collaborate on data-driven projects to apply ETL skills in a broader data science context. If you are interested in getting into this field then check out this video about Mohanaraj, and how he upskilled from ETL to Data Science with the help of Intellipaat’s Advanced Certification in Data Science and AI.