Monimuuttujamenetelm t 1 (Multivariate Data Analysis - Finnish Course)

CAMO has more than 20 years experience in providing multivariate data analysis software, products and solutions. A recognized name in the chemometrics software domain, CAMO software solutions help clients understand the knowledge hidden inside data.

CAMO provides professional training and applications development in multivariate statistical analysis, spectroscopy, experimental design, optimization and formulations, sensory analysis and PAT, worldwide.

Training options include public courses, in-house and online workshops, to bring you the practical knowledge necessary to conduct research and interpret analyses on a daily basis, to best suit your requirements.

Why CAMO Chemometrics & Multivariate Analysis training?

Tailored to meet the needs of professionals and students, each course is designed around real-world industry applications of chemometrics and multivariate analytical methods, with practical skills building and intensive, hands-on training using The Unscrambler software.

Carefully selected course exercises and application examples provide students with high-impact theory-to-skills building knowledge transfer, enabling deep learning and lasting proficiency in applications development.

CAMO Training courses are taught in an informal and pedagogical atmosphere by experienced CAMO personnel and partners.

Who should participate in this program?

The course has been designed for individuals:

* Involved in consumer insights, R&D, product development, process optimization, quality control & monitoring.

* Working with spectroscopic instruments (NIR, FTIR, UV, UV/VIS, NMR, DAS, Raman, Mass Spectroscopy) chromatography instruments (LC, CE, GC, HPLC), production data & sensory data, R&D, quality control or production processes.

* No prior knowledge of The Unscrambler or is required to attend our courses.

Course Topics

# When are multivariate methods useful?
# Principles and applications of:
# Principal Component Analysis (PCA)
# Multivariate regression: Multi Linear Regression (MLR), Principal Component Regression (PCR), Partial
# Least Squares (PLS)
# Relevant data collection
# Multivariate modeling step by step
# Pretreatment and scaling
# Detecting and dealing with outliers
# Calibration, validation
# Prediction
# The different validation methods
# Basic rules for successful data analysis