Data-Driven Decision Making for Engineers

Details

1-day course

CPD Credit: 6.5 hours; Competence/s: 1

Course Delivery Options

Calendar639207622174621818 Open Course: Available on scheduled dates. Contact cpdtraining@engineersireland.ie or call 01 665 1305, or check our CPD Calendar here.

In-company training639207622492201437 In-company training: Click here for further information on in-company courses or email incompanycpd@engineersireland.ie for a customised quote.

Course Aim

Engineers are expected to justify decisions with evidence, yet many have never been taught to work with data in a statistically rigorous way. This course closes that gap using practical statistical thinking and applied data-science methods for common engineering tasks: summarising and visualising data, quantifying uncertainty, making comparisons, building simple predictive models, and communicating results clearly.

The focus is on engineering judgement and avoiding common analytical pitfalls (e.g., misleading averages, spurious correlations, and overconfident conclusions) rather than on becoming a full-time data scientist. Examples are drawn from design, reliability, and maintenance contexts. Delegates will leave with a set of techniques they can apply immediately, and the instincts to recognise when a trend is real versus when it is simply noise.

Course Overview

The aim of this course is to help engineers use data and basic statistical reasoning to make stronger, more defensible decisions. Delegates will learn how to describe and communicate what their data shows, how to separate signal from variation, and how to make comparisons that hold up under scrutiny. The course is built around typical engineering questions such as: “Is this change real or just variation?”, “Which of these two processes performs better?”, “What is driving these failures?”, and “How reliable is this prediction?”. No prior statistics knowledge is assumed.

Course Programme

Module 1 - Data-driven engineering decisions: framing questions, understanding the data you have, and common pitfalls (misleading averages, noise vs trend)
Module 2 - Descriptive statistics: summarising and visualising data, distributions, variation, and spotting patterns/outliers
Module 3 - Comparisons & uncertainty: confidence intervals, practical significance, and an introduction to hypothesis testing
Module 4 - Relationships & drivers: correlation, regression, and how to avoid spurious conclusions
Module 5 - Worked mini case study: from messy engineering data to a decision (describe, compare, quantify uncertainty, and communicate)
Module 6 - Communicating results: decision-ready charts and narratives; wrap-up & next steps

Learning Outcomes/Objectives

Learning outcomes focus on practical competence: interpreting data correctly, separating signal from variation, quantifying uncertainty, choosing appropriate comparison/relationship methods, and communicating evidence-based conclusions in an engineering context. Outcomes are written as observable skills delegates can demonstrate at the end of the day.

Upon successful completion of this course, delegates should be able to:

Frame engineering questions clearly and identify what data would support a decision
Summarise and visualise datasets appropriately to reveal distributions, variation, trends, and anomalies
Interpret variation and uncertainty using practical tools such as confidence intervals and error bars
Apply basic hypothesis-testing ideas to compare processes or outcomes without overclaiming
Assess relationships between variables using correlation and simple regression, and recognise common traps (e.g., spurious correlations)
Communicate findings with clear charts and decision-ready recommendations for technical and non-technical stakeholders

Who Should Attend?

This course is designed for design, systems, reliability, and maintenance engineers who work with test, sensor, or operational data as part of their day-to-day role, and who need to make or justify decisions based on evidence. It is also suitable for engineering managers and technical leads who review analyses and want to ask better questions of data.

Pre-requisites: No prior statistics knowledge is needed. Delegates should be comfortable working with numbers and tables (e.g., in Excel or similar tools) and Basic Python, but the emphasis is on transferable statistical thinking and interpretation rather than programming. Familiarity with NumPy/pandas is helpful but not essential. No advanced mathematics is required beyond basic algebra.

Trainer Profile

Ali Parandeh 150x150639207621818636708

Ali Parandeh CEng is a Chartered Engineer and founder of Build Your AI. He began his career as a mechanical engineering consultant at a multinational consultancy and has led engineering teams in both large consultancies and technology start-ups. After achieving CEng status and becoming a member of the Institution of Mechanical Engineers (IMechE), he transitioned into developing AI-powered products and data analytics solutions across transport, manufacturing, engineering, finance, and cybersecurity. Over the past decade, Ali has supported organisations including the European Space Agency, HS2, and Network Rail to deploy AI in production. He is the author of “Building Generative AI Services with FastAPI” (O’Reilly, 2025) and regularly delivers UK-SPEC CPD-accredited AI workshops for the IMechE, with a focus on secure, practical adoption in safety- and privacy-constrained engineering environments.