Data Analytics and Statistics in Quantitative Chemical Analysis – Chemometric Workflow for Forensic Investigations

Data Analytics and Statistics in Quantitative Chemical Analysis – Chemometric Workflow for Forensic Investigations
Course #: SC7843
Track: Chemometrics

Course Description

The rapid advancement and increased ability to resolve chemical components has superseded the common procedures for forensic data analysis. Chemometrics is a disciplined blended in data science that aims to efficiently extract information from the expanding inventory of measurable chemicals. A methodological workflow rooted in big predictive analytics will be presented for statistically modelling changes in complex collected chemical data for applications in forensic consulting. After completion of this course, attendees will gain familiarity with concepts involving data pre-processing, statistical correlation, and multivariant statistical analysis. Case studies will be presented for the application of modern data science computer science techniques and creating supporting visuals using R and Python programming. Register for this Short Course.

1. Introduction
a. Advancements in Chemical Analysis
b. Current Approaches to Data Analysis
c. Distilling Chemical Data with Chemometrics
d. History, Key Papers, and References
2. Chemometric Approach to Data Analysis and Interpretation
a. Data Pre-processing
b. Univariant Statistics
c. Parametric and Non-Parametric Correlation
d. Multivariant Statistics
e. Principal Component and Hierachal Cluster Analysis
3. Application of Data Science in Forensics
a. Data workflow
b. Data Science in R
c. Data Science in Python
d. Visualization
4. Case Studies
a. Statistical Correlation of Contaminants
b. Chemical Fingerprinting
c. Visuals for Tracing Contaminant Source Attribution

Target Audience

The course will target intermediate data scientists interpreting complex quantitative chemical data sets. This course will also provide unique approaches to using modern programming languages and workflows to organize data analytics. Possible attendees include students, researchers, industry scientists, and data science consultants.

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