Data Analytics programme

You don’t need previous Python experience. The programme begins with spreadsheets and SQL, then introduces Python fundamentals and how to use it for analysis.

You’ll develop your coding skills through projects, connecting what you learn to questions about customers, products and business performance.

You’ll work with percentages, averages, comparisons and statistics to understand patterns and evaluate findings. The programme introduces core statistics before progressing to statistical inference and A/B testing.

You’ll apply these concepts to questions, such as whether customer behaviour differs between groups or whether an experiment supports a change. If your maths feels rusty, discuss preparation with admissions.

Yes. The programme offers flexible pacing over six to 10 months, with at least 15 hours of focused study each week.

Set aside regular time for learning, projects and reviews. Experience with customers, budgets, operations or performance reports can give you useful context for the questions you’ll investigate.

Choose based on the work you want to do:

Data Analytics: investigate business questions, build dashboards and communicate findings that help teams make decisions.

Data Science & AI: go deeper into statistics and machine learning to build and evaluate predictive models.

AI Engineering: build applications using large language models and AI agents, building on existing coding experience.

These capabilities can support different career directions. Compare the curricula and entry requirements with your background and goals.

You’ll investigate why customers leave, where people drop out before purchasing and how customer groups behave differently. You’ll also explore how to compare campaign performance and assess A/B test results.

These methods help you explain your findings and recommend what a team should investigate or change next.

Yes. You’ll complete projects that demonstrate how you analyse data, visualise findings and make recommendations. An optional independent capstone lets you develop a portfolio piece around your industry interests.

Use your work to show employers how you approach a question, choose methods and explain your conclusions.

Explore learner work in the Turing College project showcase.

Product analytics investigates how people use a product: where they encounter problems, which features they use and what keeps them returning.

Marketing analytics investigates campaign performance, customer behaviour and the steps people take towards a purchase.

Both use data to guide decisions. Choose the area that connects with your interests, existing experience and career goals.

Yes. AI tools can help write queries, create charts and draft explanations. Analytical skills help you check whether those outputs are accurate and useful.

You’ll learn to define a question, spot problems in data and judge whether a conclusion is justified. Your knowledge of a business or industry helps you connect the findings to decisions that matter.

Yes. The introductory module explores AI applications, and the Python module introduces regression and classification through predictive modelling.

For greater depth in machine learning, compare the Data Science & AI programme. If your goal is building LLM applications and AI agents using existing coding skills, explore AI Engineering.

You can work towards roles in data analytics, reporting, business intelligence, marketing analytics or product analytics. You can also apply the skills in operations, finance, marketing and other fields where interpreting data is part of the work.

Your previous experience can help you choose a direction. Understanding an industry’s customers, processes or priorities gives you context for asking useful questions and explaining the findings.

Yes. A relevant degree is one route, while other routes combine analytical skills, project work and experience from another field.

Your portfolio gives employers evidence of how you investigate questions and communicate results. Some positions require specific qualifications, so focus your applications on opportunities that match your skills and background.

A self-paced career module covers job-search strategy, your CV and LinkedIn profile. You’ll also have HR and technical mock interviews, alongside resources in the Career Guidance Hub and access to career support chat.

Use this support to prepare applications and practise explaining how your experience and project work fit the opportunities you want to pursue. Employment is not guaranteed.