Description
The amount of data in laboratories is growing ever faster, while the demands on the accuracy and efficiency of analyses are increasing. Laboratory staff must not only deliver precise results, but also be able to evaluate complex data sets quickly and effectively.
In this context, the ability to analyze and interpret data intelligently becomes a decisive competitive advantage. Laboratory managers and employees are often faced with the challenge of gaining relevant insights from a flood of information - be it to optimize processes, identify trends or ensure quality.
In this seminar, you will learn how to gain valuable insights from your laboratory data by using graphical analysis, hypothesis testing, regression, multivariate statistics, machine learning and AI. Various analysis methods are presented using practical examples. You will work through the procedures, functions and applications and learn how to interpret the results correctly. Prepare yourself for the requirements of modern data analysis and use statistical methods effectively in your day-to-day work to create concrete added value for your laboratory and your clients.
Who this seminar is aimed at
Managers from laboratories and test centers in research, development, quality control and regulatory monitoring who want to get more out of their laboratory and test data
Quality management and quality assurance specialists who want to use statistical methods and software effectively
IT staff who want to learn more about laboratory and test data analysis
Employees who carry out Six Sigma projects and want to expand their skills in statistical data analysis
After the seminar
understand the need for effective data pre-processing and know various methods for data analysis.
recognize correlations and patterns in complex data collections.
be able to test hypotheses in large amounts of data and draw valid conclusions.
know what the different analysis methods are, know their relevance for data analysis, apply them practically and interpret the results correctly.
improve your skills in process optimization and quality assurance through the use of statistical methods.
Day 1:
Basic knowledge
- Refresher on statistical basics
- Introduction to the topic of "Data analytics and data mining"
Data pretreatment
- Necessity of data pretreatment for statistical analyses
- Methods of data pretreatment
Graphical data analysis
- What are graphical analysis methods in laboratory statistics and what do they do?
- What graphical analysis methods are there?
- How do you interpret the results of graphical analysis methods?
Regression analysis
- What is regression analysis and what does it do in laboratory statistics?
- How do you apply regression analysis?
- How do you interpret the results of a regression analysis?
Analysis of variance
- What is analysis of variance and what does it do in laboratory statistics?
- How do you apply the analysis of variance?
- How do you interpret the results of an analysis of variance?
Day 2:
Discriminant analysis
- What is discriminant analysis and what does it do in laboratory statistics?
- How do you apply discriminant analysis?
- How do you interpret the results of a discriminant analysis?
Cluster analysis
- What is cluster analysis and what does it do?
- How do you apply cluster analysis?
- How do you interpret the results of cluster analysis?
Principal component analysis
- What is principal component analysis and what does it do?
- How do you apply principal component analysis?
- How do you interpret the results of principal component analysis?
Factor analysis
- What is factor analysis and what does it do?
- How do you apply factor analysis?
- How do you interpret the results of a factor analysis?
Machine learning
- How does machine learning work?
- First insights based on examples
You can find the program here
