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CourseMotorcycles & 2-Wheelers Applications open

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.

Duration3 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Organise track data into a coherent database.

02

Apply descriptive and inferential statistics to lap times and variables.

03

Programme automated analyses with Python.

04

Define performance indicators for rider and motorcycle.

05

Build and validate simple predictive models.

06

Communicate results clearly to the team.

07

Carry out a complete data analysis with conclusions.

Profile

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.
Standards & tools

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
Syllabus

6 modules

  1. 1.1Data sources: datalogger, timing and video
  2. 1.2Variable types and units
  3. 1.3CSV and MDF export formats
  4. 1.4Data quality and missing values
  5. 1.5Synchronising sources
  6. 1.6Organising a session database
  7. 1.7Metadata: circuit, weather and setup
  8. 1.8Rider data privacy
  9. 1.9The analyst's workflow
  10. 1.10Workshop: structure of a season database
  1. 2.1Descriptive statistics of lap times
  2. 2.2Dispersion and rider consistency
  3. 2.3Distributions and outliers
  4. 2.4Correlation between variables
  5. 2.5Simple and multiple linear regression
  6. 2.6Hypothesis testing between setups
  7. 2.7Confidence intervals
  8. 2.8Time series within a session
  9. 2.9Common interpretation errors
  10. 2.10Practical exercise: consistency analysis with Python
  1. 3.1Python and the Jupyter environment
  2. 3.2Reading data with pandas
  3. 3.3Cleaning and filtering signals
  4. 3.4Resampling and interpolation by distance
  5. 3.5Visualisation with matplotlib
  6. 3.6Circuit maps from GPS
  7. 3.7Reusable functions
  8. 3.8Report automation
  9. 3.9Version control with Git
  10. 3.10Lab: lap comparison script
  1. 4.1Braking and corner-exit KPIs
  2. 4.2Minimum corner speed
  3. 4.3Throttle use and time at full throttle
  4. 4.4Tyre and degradation indicators
  5. 4.5Consistency indices
  6. 4.6Benchmarking against references
  7. 4.7Monitoring dashboards
  8. 4.8Communicating results to the rider
  9. 4.9Data-driven decision-making
  10. 4.10Case study: dashboard for a race weekend
  1. 5.1Lap time prediction
  2. 5.2Degradation models
  3. 5.3Classification of riding styles
  4. 5.4Clustering similar laps
  5. 5.5Decision trees and random forest
  6. 5.6Cross-validation
  7. 5.7Overfitting and small datasets
  8. 5.8Model interpretability
  9. 5.9Limitations in racing
  10. 5.10Case study: tyre degradation model
  1. 6.1Framing the question
  2. 6.2Data collection and cleaning
  3. 6.3Exploratory analysis
  4. 6.4Selected indicators
  5. 6.5Model or statistical analysis
  6. 6.6Visualising the results
  7. 6.7Conclusions for the team
  8. 6.8Technical report
  9. 6.9Written report and presentation
  10. 6.10Final project: final review with the tutor
Methodology, software & labs

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.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2016/679, ASAM MDF and ISO 8601 applied to real cases, not as a theoretical annex.
Industry software:Python (pandas, scikit-learn), Jupyter, MoTeC i2 Pro, Power BI, Git, in free, academic or evaluation versions.
Final project:a complete track data analysis, from question and data cleaning to model, visualisation and conclusions for the team, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Framing the question
Define the performance question the analysis must answer for the team.
Data collection and cleaning
Gather, synchronise and clean the session data.
Exploratory analysis and KPIs
Explore the data and select the performance indicators to track.
Model and visualisation
Build a statistical or predictive model and visualise the results.
Final project
A technical report with conclusions for the team, reviewed by your tutor.
Career opportunities

Where this takes you

Racing data analystPerformance engineerSports data scientistTrack engineerAnalysis tools developerSports performance consultant
Admissions & scholarships

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.

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FAQ

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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