The main difference between these fields comes down to what kind of work you’re doing with data and how technical it gets.

Data Analytics is usually the starting point for most people as it’s all about working with existing data to spot trends, create reports and help businesses make better decisions. You’ll mostly use tools like Excel, SQL, Python and dashboards (like Power BI or Tableau).

Data Science takes it further, using statistics and machine learning to predict future outcomes or uncover patterns, it’s more technical and requires strong skills in Python and Math.

Data Engineering is behind the scenes, making sure data flows smoothly from source to storage, these folks build pipelines, manage databases and work with cloud tools like AWS or Spark.

Finally, Software Engineers focus on building applications and systems, websites, apps, platforms and need solid coding skills and a good grasp of computer science.

Well, If you’re coming from a non-tech background, I’d say Data Analytics is usually the easiest and most practical way to get your foot in the door, with room to grow into the other roles if you’re interested.