Turning raw data into decisions — the craft of cleaning, exploring, visualising and engineering data that sits underneath both good analysis and good AI.
Every model and every dashboard rests on data work most people never see. This topic covers that work end to end: data wrangling to make messy data usable, data analysis to answer real questions, data visualization to make findings land, and data engineering to deliver it all reliably at scale. It is also the bridge into AI — the same pipelines that feed dashboards now feed models.
Data analysis is the process of examining data to answer a question — cleaning it, exploring it, and drawing conclusions you can act on and defend.
Data wrangling is the work of turning raw, messy data into a clean, consistent shape that analysis or a model can actually use.
Data visualization is the practice of representing data graphically so that patterns, comparisons and outliers become obvious to a human eye.
Data engineering is the discipline of building the pipelines and storage that move data reliably from where it is created to where it can be analysed or fed to a model.
Python is a general-purpose programming language prized for readable syntax and a vast ecosystem of libraries, which has made it the default language of data and AI.
Closes the gap most aspiring AI engineers hit first — they can write a little code, but not the programming-and-maths groundwork that machine-learning material takes for granted. It walks from language basics to a working model in PyTorch, with data libraries in between.
About 52 hours
Walks the full investigative loop on real, untidy datasets — pose a question, gather and clean the data, explore it, then turn the result into a visual story an audience can act on. Each project lets you choose the dataset, so the work doubles as portfolio material.
About 43 hours
An advanced, four-course ExpertTrack about the unglamorous half of AI: the plumbing. It covers feeding models with well-built pipelines and warehouses, grounding them in organisational knowledge through retrieval, running them reliably at scale, and orchestrating agents that act on their own.
About 8 weeks
A year-long online Master's, not a short course — a degree for marketers who want to lead the technology rather than follow it. It pairs strategy and theory with marketing automation, SEO and a data-led, project-based finish against a real business brief.
1 year (full-time, 100% online)
