Course in data analysis in track motorcycling
Online course of 300 hours in data analysis for track motorcycle racing. 6 modules covering track data management, applied statistics, programming for analysis, performance indicators and predictive models, with an assigned technical tutor.
Course in data analysis in track motorcycling — studying from the United Kingdom
The motorcycle and two-wheeler sector in the United Kingdom includes manufacturers, specialist component suppliers and a strong motorsport and leisure market. Triumph, with its major plant in Hinckley, and specialist firms across the Midlands and the South West contribute to design, engineering and production. Electric two-wheeler development and scooter markets are expanding alongside traditional motorcycle engineering. The Driver and Vehicle Standards Agency and the Motorcycle Industry Association support standards and regulation in this area.
Fees for the United Kingdom: £537 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Are there UK professional bodies that recognise motorcycle engineering training?
The UK has no single regulated title for motorcycle engineers. Relevant bodies include the Institution of Mechanical Engineers and the Institute of the Motor Industry, which offers its own qualifications. SEIUM's courses are private and not accredited by any national body, so recognition by these organisations would be their decision and is not guaranteed.
Do UK motorcycle manufacturers, dealers or suppliers hire people with private engineering qualifications?
Hiring in the UK motorcycle sector typically values hands-on technical experience, workshop skills and relevant engineering knowledge. A private online qualification may help show specialist learning, especially alongside a strong final project, but employers decide how much weight to place on it. Ask prospective employers directly how they view such training.
How long does the programme take, and can I study while working in the UK?
Specialist courses require 300 hours, postgraduate diplomas 800 hours and master's programmes 1,900 hours. All study is online in English with an assigned technical tutor. You can study from anywhere in the UK without a visa, and fees can be paid in instalments. Employer sponsorship is possible if your employer agrees to support you.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Organise track data into a coherent database.
Apply descriptive and inferential statistics to lap times and variables.
Programme automated analyses with Python.
Define performance indicators for rider and motorcycle.
Build and validate simple predictive models.
Communicate results clearly to the team.
Carry out a complete data analysis with conclusions.
Who it's for
- Racing data analysts.
- Performance engineers working with riders and teams.
- Sports data scientists moving into motorsport.
- Track engineers who want stronger statistical and programming skills.
- Developers of analysis tools for racing.
- Sports performance consultants.
- Engineering or science graduates aiming to join racing teams.
What you'll work with
- Regulation (EU) 2016/679 (general data protection regulation)
- ASAM MDF (measurement data format)
- ISO 8601 (representation of dates and times)
- ISO/IEC 27001 (information security management)
- FIM Superbike World Championship Regulations (technical and sporting rules for WorldSBK)
- Python (pandas, scikit-learn)
- Jupyter
- MoTeC i2 Pro
- Power BI
- Git
6 modules
1.1Data sources: datalogger, timing and video1.2Variable types and units1.3CSV and MDF export formats1.4Data quality and missing values1.5Synchronising sources1.6Organising a session database1.7Metadata: circuit, weather and setup1.8Rider data privacy1.9The analyst's workflow1.10Workshop: structure of a season database
2.1Descriptive statistics of lap times2.2Dispersion and rider consistency2.3Distributions and outliers2.4Correlation between variables2.5Simple and multiple linear regression2.6Hypothesis testing between setups2.7Confidence intervals2.8Time series within a session2.9Common interpretation errors2.10Practical exercise: consistency analysis with Python
3.1Python and the Jupyter environment3.2Reading data with pandas3.3Cleaning and filtering signals3.4Resampling and interpolation by distance3.5Visualisation with matplotlib3.6Circuit maps from GPS3.7Reusable functions3.8Report automation3.9Version control with Git3.10Lab: lap comparison script
4.1Braking and corner-exit KPIs4.2Minimum corner speed4.3Throttle use and time at full throttle4.4Tyre and degradation indicators4.5Consistency indices4.6Benchmarking against references4.7Monitoring dashboards4.8Communicating results to the rider4.9Data-driven decision-making4.10Case study: dashboard for a race weekend
5.1Lap time prediction5.2Degradation models5.3Classification of riding styles5.4Clustering similar laps5.5Decision trees and random forest5.6Cross-validation5.7Overfitting and small datasets5.8Model interpretability5.9Limitations in racing5.10Case study: tyre degradation model
6.1Framing the question6.2Data collection and cleaning6.3Exploratory analysis6.4Selected indicators6.5Model or statistical analysis6.6Visualising the results6.7Conclusions for the team6.8Technical report6.9Written report and presentation6.10Final project: final review with the tutor
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the course is compatible with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Jupyter, MoTeC i2 Pro, Power BI and Git, in free, academic or evaluation versions. Exercises are based on real procedures for statistical analysis of lap times, tyre degradation models and performance dashboards. Assessment is continuous by module, plus the final project.
Standards covered: Regulation (EU) 2016/679; ASAM MDF; ISO 8601; ISO/IEC 27001; FIM Superbike World Championship Regulations.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No prior qualification is required.
- An internet connection and a computer able to run the programme's software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python (pandas, scikit-learn), Jupyter, MoTeC i2 Pro, Power BI, Git, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No prior qualification is required; you need a computer able to run the software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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