The day-to-day work of your engineers often involves statistical issues. Sometimes these are recognized as such, sometimes not. It’s not always clear which technique should be applied, and when. Or how to apply the techniques in the right way and draw the correct conclusions. If any of this sounds familiar, then 'Applied Statistics' training may well be what you’re looking for!
Such issues have undoubtedly become more prevalent in recent years. Companies gather more and more data during production, or log more data from devices while they’re operational at clients. But how does one extract knowledge out of the large amounts of data created in this way? As well as traditional statistical techniques, there are also many modern Machine Learning techniques now available.
This training course makes participants proficient in statistics and machine learning techniques, teaches them which techniques to use in which situations, and provides an overview of the differences between the various techniques. In addition, we teach participants to translate the question from the language of the business into a statistical/machine learning question, solve this problem and then translate the mathematical solution back into a solution in the language of the business.
The training is always customized and tailored to your needs and circumstances. To give you an idea, here is an example of a training program made up of two parts:
- Part 1: Applied statistics for data analysts
- Part 2: Advanced statistics and machine learning
Each part might in this case consist of 6-7 blocks of a half-day each:
- Recap statistics and probability theory, intro software tools, regression and correlation.
- Measurement System Evaluation, process capability.
- Q&A, CQM cases, cases participants.
- Significance testing.
- Principles of Design of Experiments.
- Intro to auto-correlated data, Q&A, typical approaches of your company in the context of the statistical topics studied, participant cases.
- Recap Part 1 training, Optimal Designs.
- Spread breakdown part A.
- Spread breakdown part B.
- Q&A, real company cases.
- Machine Learning part A.
- Machine Learning part B.
- Q&A, participant cases.
By the end of the training participants:
- recognize the standard questions that arise in industrial environments;
- can translate such business questions into statistical and machine learning questions;
- know the necessary statistical and new machine learning techniques, and which techniques to apply when;
- can apply these techniques in statistical software;
- know how to interpret the results of these techniques and translate them into their daily work;
- are trained in applying and interpreting the most frequently used statistical and machine learning techniques in their daily work;
- are trained for a number of issues, that are complex yet typical of those your organization faces, in (i) addressing the question using statistical techniques, (ii) applying the related statistical and machine learning techniques, and (iii) the mathematical background to those techniques.
Interested in tailor-made training in Applied Statistics & Machine Learning?
Contact Bert Schriever