(Office Hours: Sims 312A TR 4:00-5:15)
Course Outline: This is a short course in Introductory Chemometrics, the application of mathematical and statistical techniques for the analysis of chemical data sets. With the tremendous increase in data collection and processing capabilities, the rate of data generation using modern analytical instruments can be overwhelming. Chemometrics rescues us from the situation in which we are drowning in information but starving for knowledge.
The goal of many chemometric techniques is to use measurements to produce a model for any one of a nearly infinite number of possibilities to include defining a complex system, predicting properties, optimizing a signal, designing an experiment, immediately assessing the quality of a product from an industrial process or proving an important hypothesis. Most research projects require the understanding and judicious use of statistical and mathematical tools we will be learning in this course. While technologies that generate data will continue to evolve, the mathematical and statistical tools available will continue to remain "current." Understanding and using these is an increasingly important part of a science education.
These mathematical and statistical tools are useful for a broad range of applications, particularly those that involve working with large data sets. Applications include solving problems such as apportioning the hydrocarbon air pollutants in a region to specific sources, controlling a major industrial chemical process, evaluating the impurities present in a pharmaceutical product, and determining the amount of moisture in wheat from a satellite. One can even apply these tools to determine the most powerful counting system to use in the game of 21!!
The course begins with a block on the design of calibration experiments that includes the three primary types of models used for analyte quantification: calibration, internal standard and standard addition. This block includes an introduction to sum of squares parameters, design matrices, and multivariable mathematical models of experimental data. This is followed by a review of descriptive and inferential statistics. The last half of the course focuses upon multivariate data sets, matrix techniques, and powerful mathematical tools such as principle component analysis that are widely used across a wide array of science and engineering disciplines for pattern recognition, data mining, quantification, prediction, and experimental design.
Computer Expertise: This course will require students to demonstrate a capability to rapidly analyze data sets to address particular questions using Excel and Matlab. Tests and final exams will be primarily performance based, time-limited, assessments.
Schedule: Lectures are scheduled at the appointed hour in the assigned classroom. The course syllabus provides the specific schedule. All course information is posted on the chemistry department's web page (chem.winthrop.edu).
Class Preparation: Homework assignments from the previous lesson are to be turned in at the beginning of each class. You are responsible for all assigned material and for all material discussed in lecture. You are expected to complete each reading assignment and begin working on the assigned problems prior to the date listed in the syllabus. For each class I recommend that you do the following:
Review previous lecture notes and course competencies
Complete the assigned problems from the previous lesson
Read assigned lesson for upcoming lecture, take notes
Work assigned problems
Graded Exercises
Class quizzes and homework assignments will be worth 10-20 points each. Individual work is required on homework assignments.
Each problem set requires individual work and will be worth 30 points.
The final will be cumulative and worth 100 points. The final will be a two-part exam that will include a closed book exam and an open book, computer lab portion to demonstrate proficiency and understanding of material covered throughout the course.
Grades: Percentages will be calculated based upon total earned points divided by total points tested. You must score better than 50% on the final exam to pass the course. You must score an A on the final exam to earn an A in the course. The following grade range will be used: A = 93-100%; A- = 88-92%; B+ = 85-87%; B = 80-85%; B- = 76-79%; C+ = 72-75%; C = 66-71%; D = 56-66%; F = <66%
Attendance: You are expected to attend all class meetings for the full scheduled time.