Getting Started with Data

Delivery type Study 100% online
Start November 2026, March 2027
Length 8 weeks (150 hours)
Price £995

About this course

As data and AI increasingly shape how organisations operate, this fully online CPD course will enable you to make more informed, evidence-based decisions and use data more effectively in your role.

This standalone module empowers you to confidently engage with data in your work and professional life. It is designed specifically for those who are new to quantitative methods, the module takes an intuitive, accessible approach to the technical language commonly used for data science, analytics and data interpretation.

Build essential data literacy and analytical thinking skills with this practical online data course for professionals - no prior experience in maths, statistics or programming required.

Get course guide

This accessible, practical 15-credit module is delivered by a world top 100 (QS 2027) Russell Group university and is designed to build your confidence week by week.

You'll work with real-world examples from areas such as healthcare,  business and urban analytics, and engage directly with academics through structured learning, feedback and discussion - offering more support than self-paced courses.

The course focuses on interpreting data rather than complex mathematics, teaching you to confidently understand and adapt dashboards, reports and data visualisation outputs in R and Python. You will also explore the foundations of applied data science, developing an understanding of how data-driven approaches can support decision-making in real workplace situations. You will develop practical data interpretation skills and learn how data insights can be used effectively, focusing on interpreting results rather than developing programming expertise.

The module includes two assessments: a short task in week 3 and a final coursework due at the start of week 8. In week 3, you’ll record an audio analysis of a real problem-solving example, focusing on technical language, evaluating approaches and spotting errors. For the final coursework, you’ll select a report or dashboard, explain how its code works, and adapt it to create a new example.

This course is designed for professionals looking to upskill and apply their learning directly within their current roles.

Why study with Leeds

World Top 100 University

Study at an internationally renowned Russell Group university, ranked 77th in the QS World University Rankings 2027.

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100% online study

Study completely online and part-time, fitting your studies around your existing work and life commitments.

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Connected learning

Enjoy your studies in a world-class online learning environment, with regular live interactive sessions and global peer networking.

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Industry leader

This course is taught in conjunction with the Leeds Institute for Data Analytics (LIDA), a leading research institute dedicated to data-driven innovation.

Advance with purpose

This course provides recognised postgraduate credits that you'll be able to use to support your professional development. It’s designed to help you grow your expertise with purpose and continuity.

Entry requirements

At least 6 months of relevant professional experience in a role involving data-informed decision-making, or a 2:2 honours degree in any subject.

English language requirements

IELTS 6.5 overall, with no less than 6.0 in any component.

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Learning outcomes

On completion of this 8-week course, you will be able to:

Analyse

Explain and apply fundamental statistical concepts to analyse and interpret real‑world data.
Simulate

Demonstrate how uncertainty and variability can affect observed data by applying Monte Carlo simulation techniques.  
Report

Load, execute, edit and interpret existing dashboards, and reports to extract relevant insights and generate reports for decision-making purposes.  
Solve

Take a structured and logical approach to solving statistical and coding problems with data.
Produce

Summarise the content of data science reports and dashboards clearly and concisely using both non-technical and academic language.

Your course leader

Jenny is a Data Science Lecturer at the Leeds Institute for Data Analytics (LIDA), where she teaches across the MSc Data Science (Statistics), MSc Ethical AI and Society, and leads the “Data Analytics for a Sustainable World” module for Leeds International Summer School, as well as CPD courses including 'Getting Started with Data'.

"We want to equip learners with the core foundational skills and confidence to recognize and question the assumptions, choices, and limitations behind any data science model. Building hands-on practice into every unit, so learners finish the module and contribute meaningfully to data-driven decisions in their work."

Jenny Sexton, Data Science Lecturer, Leeds Institute for Data Analytics.

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Explore the curriculum

Explore the six, in-depth units that make up the curriculum of our Getting started with Data CPD course.

Understanding Data and Probability

Learn how discrete random variables and probability distributions help model real‑world experiments. Using a simulated medical trial, you’ll explore how results change across repeated runs and build your own datasets to calculate key summary statistics.

Exploring Patterns and Uncertainty in Data

Discover how continuous random variables and cumulative distribution functions can model rainfall and flood‑risk data. You’ll work with real datasets, explore extreme events, and practise adapting simple scripts to analyse continuous data.

Estimating Parameters in Real-World Data

Explore how the method of moments helps estimate key parameters in queuing systems, using a hospital patient‑flow simulation. You’ll calculate theoretical moments, run estimation scripts, and build confidence interpreting what these parameters mean for system performance.

Comparing Approaches to Estimation

Learn maximum likelihood estimation through a case study on student engagement and exam performance. You’ll compare different estimation methods, strengthen your understanding of core statistical ideas, and sharpen your coding clarity and interpretation skills.

Modelling Systems and Relationships with Data

Use Markov chains to model traffic flow and investigate long‑term congestion patterns. You’ll work with transition matrices, debug simulation code, and learn to communicate the strengths and limits of Markov‑chain models clearly.

Questioning Data Models and their Limitations

Revisit the medical‑trial dashboard to predict patient outcomes using linear and logistic regression. You’ll explore confounding factors, assess model reliability with resampling methods, and gain insight into designing effective, evidence‑led dashboards and reports.

Your professional development, continued

Explore additional courses that build on the skills you’ve gained here and support your ongoing professional development.

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