This course enables you to perform data mining and apply machine learning tools and provides you with an understanding of how to deal with data that does not comply with the normal assumptions. We will cover and discuss data from advanced design of experiments (DoE) studies to machine learning applications.
The tools included in this course are handpicked from our daily work. These tools are our go-to tools for more advanced data analyses.
We recommend that you take courses in Basic Statistics, Data Quality, Statistical Modelling and Operational Design Of Experiments prior to this course. In that way you can follow a complete analytical roadmap for achieving true manufacturing intelligence in your organization.
This course will cover:
- Hypothesis testing on non-normal distributed data
- Fitting asymptotic curves
- Time series analysis
- Cross validation
- Principal component analysis (PCA)
- Partial least square (PLS)
- Advanced DoE - with whole plots and variance modelling
- Machine learning
- Decision trees
- Boot strap forest
- Support vector machine
- Neural network (NN)
The software used in this course is JMP from SAS and the instructors will demonstrate all the exercises in JMP. For some advanced modelling tools, exercises will be demonstrated in JMP Pro version by instructors. If you are currently not a JMP user, we recommend you install JMP on your computer before participating in the course. A free 30-day trial can be found in JMP homepage. If you need assistance in installing JMP or have any questions do not hesitate to reach out to email@example.com.
If you are using another statistical tool, we can arrange a course using your preferred tool. Contact us to hear more about this possibility.
Venue: NNE's headquarters, Bredevej 2, 2830, Virum, Denmark
Price: DKK 6,000 + VAT (includes course materials, refreshments and lunch)
Language of instruction: The course will be held in English unless all participants speak and understand Danish
Cancellation disclaimer: We reserve the right to cancel the course due to instructor illness or low participant numbers. If this is the case, we will inform you in good time and offer a full refund where applicable.
Duration: 1 day (08:30-16:30)
- 14 November 2022 and 29 March 2023