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DATA SCIENCE

Data analysis and statistics

Modern production-related measurement systems often generate enormous amounts of data. But which parameters allow for the stability of a process to be checked? What strategies or methods of analysis are necessary to prove or disprove a hypothesised relationship? Can the relevant in-process variables be increased, with sufficient security, from the data collected? These are typical questions that frequently arise in this context and which we are able to answer for you.

Problems with data do not only arise in production, they also occur in the development of new products that often have a long series of tests to be analysed. A typical example of this is when aiming to demonstrate the superiority of a new approach. In the pharmaceutical industry, such tests are commonplace, however in other industries a long series of measurements are often the beginning of a new development or are the basis for the evaluation of a solution, for example in the testing of a prototype.

The importance of applied statistics comes from the analysis that can be done on already collected data and as a basis for planning measuring methods. This involves, for example, considering how large a sample must be in order to verify or disprove a particular statement. It also plays an important role in the selection of a suitable measurement process for the statistics.

Benefit from our extensive experience in the field of data analysis and applied statistics and put this knowledge to your use. With our help, we can transform your data to focus it towards the results you want. We’ll analyse your data for you, whether it be a single analysis or something that requires the development and implementation of data analysis algorithm  to give you daily oversight.

 

 

Our services:

  • Statistical analysis and machine learning
  • Data analysis, methods, and strategies for analysis of large data sets
  • Parametric studies, planning of measuring sites, and measurement methods
  • Development of data analysis algorithms

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