Description
The volume of data in laboratories is growing at an ever-increasing rate, while the demands for accuracy and efficiency in analysis are rising. 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 intelligently analyze and interpret data is becoming a decisive competitive advantage. Laboratory managers and staff often face the challenge of extracting relevant insights from a flood of information—whether to optimize processes, identify trends, or ensure quality.
In this seminar, you will learn how to extract valuable insights from the data you collect in the lab by using graphical analysis, hypothesis testing, regression, and multivariate statistics, all the way through to machine learning and AI. Various analytical methods will be presented using practical examples. You will explore the procedures, mechanisms, and applications of these methods and learn how to interpret the results correctly. Get ready to meet the demands of modern data analysis and effectively apply statistical methods in your daily work to create tangible added value for your lab and your clients.
Who is this seminar intended for?
Managers from laboratories and testing facilities in research, development, quality control, and regulatory oversight who want to get more out of their laboratory and test data
Professionals in quality management and quality assurance who want to use statistical methods and software effectively
IT professionals seeking to advance their skills in the field of laboratory and test data analysis
Employees who are implementing Six Sigma projects and wish to expand their expertise in statistical data analysis
After completing the seminar "
"
you will understand the importance of effective data preprocessing and be familiar with various methods of data analysis.
you will recognize correlations and patterns in complex data sets.
you will be able to test hypotheses in large data sets and draw valid conclusions.
you will know what the various analysis methods are, understand their relevance for data analysis, apply them in practice, and interpret the results correctly.
you will improve your skills in process optimization and quality assurance through the use of statistical methods.
Day 1:
Basic Knowledge
– Review of statistical fundamentals
– Introduction to the topics of “Data Analytics and Data Mining”
Data Preprocessing
– The Need for Data Preprocessing in Statistical Analyses
– Methods of Data Preprocessing
Graphical Data Analysis
– What are graphical analysis methods, and what do they accomplish in laboratory statistics?
– What graphical analysis methods are available?
– 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 is regression analysis applied?
– How are the results of a regression analysis interpreted?
Analysis of Variance
– What is analysis of variance, and what does it do in laboratory statistics?
– How is analysis of variance applied?
– How are the results of an analysis of variance interpreted?
Day 2:
Discriminant Analysis
– What is discriminant analysis, and what does it do in laboratory statistics?
– How is discriminant analysis applied?
– How are the results of a discriminant analysis interpreted?
Cluster Analysis
– What is cluster analysis and what does it do?
– How is cluster analysis applied?
– How are the results of cluster analysis interpreted?
Principal Component Analysis
– What is principal component analysis and what does it do?
– How is principal component analysis applied?
– How are the results of principal component analysis interpreted?
Factor Analysis
– What is factor analysis and what does it do?
– How is factor analysis applied?
– How do you interpret the results of a factor analysis?
Machine Learning
– How does machine learning work?
– An introduction through examples
You can find the program here
