Engineering Data Processing with Python

Details

1-day course

CPD Credit: 6.5 hours; Competence/s: 1

Course Delivery Options

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

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

Course Overview

The aim of this course is to equip delegates with practical, hands-on approaches to build reliable data processing workflows for engineering datasets in Python. Delegates will learn how to ingest common file exports (Excel/CSV), standardise and reconcile inconsistent fields, handle missing and duplicate records, apply basic quality checks, and produce clean, structured outputs that can feed into reporting, analytics, or downstream automation.

The course is deliberately focused on the day-to-day realities of engineering data rather than abstract examples.

Course Programme

Module 1: Course setup and engineering data reality check (common failure modes, reproducibility, defining clean enough)
Module 2: Loading engineering datasets in Python (CSV/Excel, data types, parsing dates, basic profiling)
Module 3: Cleaning and standardising tabular data (missing values, inconsistent formatting, units, categorical cleanup)
Module 4: Deduplication and record linkage basics (keys, fuzzy issues, safe merge strategies)
Module 5: Combining data from multiple sources (joins, appends, schema alignment, audit columns)
Module 6: Data quality controls (validation rules, sanity checks, simple anomaly flags, logging outputs)
Module 7: Packaging a repeatable processing script (functions, configuration, input/output structure)
Module 8: Mini-capstone: build an automated processing workflow on a realistic engineering dataset

Learning Outcomes & Objectives

Learning outcomes focus on what delegates can do at the end of the course: independently clean, validate, and transform messy engineering datasets in Python, and implement those steps as a repeatable script that reduces manual effort and error.

The course is practical and outcome-driven, with an emphasis on realistic Excel/CSV-style operational datasets from manufacturing, rail, infrastructure, and aerospace contexts, and on building processing steps that are reliable and easy to rerun.

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

Load common engineering data exports (CSV/Excel) into Python and quickly profile shape, types, and obvious quality issues.
Clean and standardise messy tabular data, including handling missing values, inconsistent formatting, and basic unit/field normalisation.
Identify and resolve duplicate records safely using practical deduplication strategies.
Combine datasets from multiple sources using robust merge and reconciliation techniques.
Implement lightweight data quality controls (validation rules and sanity checks) to catch problems before analysis.
Produce clean, structured outputs suitable for reporting, analytics, or downstream automation.
Write a repeatable Python processing script (functions and clear inputs/outputs) so the workflow can be rerun reliably.

Who Should Attend?

This course is aimed at engineers and technical leads responsible for operational data, asset management systems, or analytics workflows in manufacturing, rail, infrastructure, or aerospace environments. It is suitable for engineers, analysts, and technical professionals who need to turn messy exports into reliable datasets, and who want a practical, Python-based approach they can apply immediately.

Prerequisites: a working knowledge of Python is assumed. Basic familiarity with tables/files (Excel/CSV) is helpful. The Foundation Python course covers the prerequisites if needed.

Trainer Profile

Ali Parandeh 150x150639203329647732972

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.