Course Description
Industrial data science applies quantitative and machine learning methods to the specific data generated by industrial and manufacturing processes — sensor streams, process parameters, cycle times, yield rates, and failure records. This foundational course establishes the core concepts, methods, and workflow of data science in an industrial context.
Unlike generic data science training, this course is grounded in industrial data and applications: predictive maintenance, quality prediction, process monitoring, and asset optimisation. Participants build the essential foundation — data understanding, statistics, core machine learning, and the data science lifecycle — needed to progress into specialised industrial AI and analytics roles.
What you will achieve
By the end of this training course, participants will be able to:
- Explain what data science means in an industrial context
- Understand the industrial data science lifecycle
- Apply descriptive statistics and data exploration to industrial data
- Distinguish supervised and unsupervised learning and their industrial uses
- Understand core machine learning models and when to use them
- Recognise the characteristics and challenges of industrial data
- Frame industrial problems as data science problems
- Understand the role of physics-informed and hybrid models
How the course is delivered
The course combines data science fundamentals with industrial examples and datasets drawn from manufacturing, energy, and process industries. Concepts are introduced from foundations and applied to realistic industrial problems.
The programme balances statistical and machine learning theory with the practical realities of industrial data.
Designed for
This training course is ideal for:
- Engineers entering industrial data science
- Reliability, process, and quality engineers
- Technical professionals building analytics capability
- Aspiring industrial data scientists
- Anyone needing a grounded introduction to industrial data science
Daily programme
- Data science in the industrial context
- The industrial data science lifecycle
- Types of industrial data
- Framing industrial problems as data problems
- Descriptive statistics for industrial data
- Data exploration and visualisation
- Data quality and preparation
- Handling missing and noisy sensor data
- Supervised learning for classification and regression
- Unsupervised learning and clustering
- Model evaluation and validation
- Overfitting and generalisation
- Predictive maintenance and fault classification
- Quality and yield prediction
- Process monitoring and anomaly detection
- Physics-informed and hybrid models
- The data science workflow in practice
- Communicating results to engineers and management
- Common pitfalls in industrial data science
- Case study workshop and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
