Discover how to use PythonTM for spectral data processing
Python
1,5 days
Researchers, scientists and engineers
Objectives
This training session in Python for spectral data analysis is intended for people wishing to develop models of chemometrics and Machine Learning using Python.
Objective is to become comfortable with using Python to implement the following techniques
- exploratory analysis (Principal Component Analysis or PCA)
- regression or quantitative prediction, such as MLR (Multiple Linear Regression), PCR (Principal Component Regression) and PLS (Partial Least Square Regression)
- spectroscopic preprocessing, unavoidable to optimize the spectra analysis.
During this training, focus is made on the practical use of data analysis methods and the different steps necessary to analyze the spectroscopic data with Python language.
For each method, application exercises are proposed with Python scripts provided by Ondalys.
Need a specific training?
Our team study your request to offer you the most suitable and personalized training.
Program
Half day 1: Introduction to PythonTM for Machine Learning
- Introducing some PythonTM libraries for Machine Learning
- Using the Anaconda distribution
- Using Notebook (Jupyter, JupyterLab)
- Practice
- Practice
Day 2: Dataset application exercises with existing PythonTM scripts
- Principal Component Analysis (PCA)
- Linear multivariate regression models (PLS)
- Pre-processing of spectroscopic data
- Conclusions on multivariate data analysis
- Practice
Our trainer team
They talk about us
« Ondalys, skilled in teaching how to analyze spectroscopic data! »
Ondalys enables our Bruker customers to deepen their understanding of their processes and optimize their quality expectations. Ondalys does not hesitate to share its knowledge and skills in the analysis of spectral data. So it is always with pleasure and serenity that I recommend the friendly team Ondalys to Bruker customers.Our expertise for the analysis of your data
With more than 15 years of experience in data analysis (Chemometrics and Machine Learning), in particular applied to measurements from spectrometers, the experts of our teams support you at each stage of your projects.
