Chemometrics; Practical Approach to Data Analysis
During your chemical career, you will generate a lot of data. Only with advanced data analysis methods can this data - tsunami - be made insightful and interpreted further. During the course you will be introduced to the most important chemometric data analysis methods currently available and how they may benefit interpretation of analytical chemical data. Analytical chemical instruments can record massive amounts of data in just a small fraction of time, which need to be analysed for instantaneous management decisions. Such volumes of data need to be processed by advanced data analysis methods. The power of such methods lies in their multivariate nature. For example, an infrared spectrum may consist of a thousand absorbances measured at different wavelengths. While traditional data analysis methods focus on analysis of each wavelength separately, advanced chemometric methods focus on the entire spectrum at once, thereby taking much more information into account. The research field of chemometrics focuses on the development and improvement of advanced data analysis methods for the analysis of chemical data, including e.g. the removal of instrumental artifacts by data pre-processing , the prediction of sample properties, and comprehensive assessment of significance by validation .
Chemometrics as an interdisciplinary field is applied in various areas:
- Analytical Chemistry
- Pharmaceutical and Medical Science
- Forensic Science
- Clinical Science-Disease Diognosis
- Environmental Monitoring
- Fermantation Technology
- Food and Industrial
- Email : email@example.com
- Telephone : +98-21-66165329
- Fax : +98-21-66029165
- Address : Department of Chemistry, Sharif University of Technology, Azadi Ave., Tehran, Iran
WSC-2022 is the third, 4-day winter school of chemometrics held in Sharif University of Technology, Iran. It is designed with the aim of introducing different key aspects of advanced data analysis in science. WSC-2022 addresses not only to chemistry students, but also to any individual who wants to acquire basic knowledge on multivariate data analysis from different disciplines (Physics, Biology, Geology, Environmental Sciences, Forensic Sciences, etc.). It is also practical for researchers working in research laboratories or industry who want to apply advanced data analysis in their daily research environment.