Data and AI training: roles, skills and course paths

Data analysts prepare data, define measures and explain a result to people who need to act on it. SQL, spreadsheets, dashboards and clear communication matter as much as a visualisation tool. A good starter project answers one practical question, such as why a subscription metric changed or which channel is producing qualified enquiries.

Data science needs a stronger statistical foundation. The work includes framing a question, cleaning a sample, selecting a method and checking whether a result holds up. Python is common because it supports analysis and modelling. A course should give you practice explaining assumptions, limits and evaluation measures instead of only producing a model score.

AI engineers connect models to products and operating systems. They need software development skills alongside an understanding of model behaviour, testing, deployment and monitoring. A useful portfolio item might be a small service that accepts a request, applies a model and records when its output needs review. This path usually suits people who enjoy building and maintaining software.

• Choose analytics for reporting, business questions and measurable operational insight.
• Choose data science for statistical investigation and predictive modelling.
• Choose AI engineering for software delivery, integrations and model operations.
• Check that the course includes projects you can discuss in an interview.