#### Course Goals

Chem 351 provides an introduction to how we can use mathematical techniques to extract useful information from data. As with any area of chemistry, the field of chemometrics is too broad to cover in a single semester; our content-specific goals, which necessarily are modest, are to learn how to:

• characterize and visualize data using descriptive statistics
• use basic statistical tools to make comparative decisions about the results of an experiment
• build mathematical models to predict a response based one or more dependent variables
• enhance the quality of analytical signals, including separating the analytical signal from background noise
• find patterns in data and use them for qualitative or quantitative purposes

In addition to these content-specific goals, we have several broader goals; these are to:

• understand the importance of well-planned experiments that yield useful data
• appreciate the rich amount of information hidden within data
• gain familiarity with using the software package R as a tool for processing and analyzing data
• become proficient in reading chemical literature

#### Textbook and Other Resources

The primary text for this course is Analytical Chemistry 2.1, a free digitial textbook that provides a broad introduction to analytical chemistry. You can download the text, or selected chapters, using this link. Reading assignments from the text are provided on the course's class schedule page. Additional resources are available on either the course's class schedule page or the course's archive page.

An important part of the course is learning to use the software package R to process and to analyze data; thus, you will need regular access to a laptop computer during class.

#### Course Structure

In general, the course follows a simple pattern: we first introduce a new chemometric method by focusing on its basic mathematical theory, using a simple data set to illustrate concepts and calculations. Before the next class session you will complete a short problem set of one or two problems. The purpose of a short problem set is to appreciate and understand the relevant calculations using a data set that is sufficiently small that you can tackle it using no more than a pen, some paper, and a calculator.

A small data set, of course, does not have sufficient complexity to demonstrate the nuances of and the power of a chemometric method; thus, after we review your work on the short problem set, we will learn how to use R to work with more complex data sets. Finally, a long problem set will provide you with the opportunity to tackle more realistic problems.

In addition to completing short problem sets and long problem sets, you also will complete three take-home exams.

• short problem sets: 5%
• long problem sets: 20%
• first exam: 25%
• second exam: 25%
• third exam: 25%

Letter grades are assigned using the following scale:

 A (>92) A- (92-90) B+ (89-87) B (86-83) B- (82-80) C+ (79-77) C (76-73) C- (72-70) D+ (69-67) D (66-63) D- (62-60) F (<60)

Final averages are not rounded; to earn a grade of B instead of a grade of B-, for example, you need a final average of ≥ 83.0. These ranges are fixed with the following caveat:

• At the instructor's discretion, a grade on the borderline may be increased or decreased by a maximum of one point to account for intangible factors. For example, a 79.7 (C+) may become an 80.7 (B-) or an 80.7 (B-) might become a 79.7 (C+). Intangible factors include, but are not limited to, a particularly strong or weak final exam, a steady improvement or decline in your performance during the term, or a particularly strong or weak contribution to the class or lab. These adjustments are not common.

#### Office Hours

Feel free to stop by my office (Julian 364) without an appointment at any of these times:

• Monday from 9am - 10am and from 2pm - 3pm
• Tuesday from 2pm - 4pm
• Wednesday from 9am - 10am and from 7pm - 9pm
• Thursday from 2pm - 4pm
• Friday from 9am - 10am and from 2pm - 4pm

If you wish to schedule an appointment at another time, please catch me after class or lab, send me an email, or drop by my office.

#### Due Dates

Because I value thoughtful, well-written and well-reasoned work more than absolute deadlines, the due dates for most assignments are intentionally flexible. Unless otherwise specified, there is no penalty for turning in an assignment late if I am still in the process of grading the assignment; however, once I finish grading a set of assignments, any missing work receives a grade of zero (no exceptions).

Flexibility in due dates is not a license to procrastinate. To make this policy work requires communication. If you need additional time, then you must meet with me before the assignment's due date so that you can show me the work you have completed and explain what work remains and when you believe you will be prepared to turn in the assignment. Together we will work on a reasonable extension.

#### Attendance

A textbook is a poor substitute for the active learning that takes place in classroom. Although attendance for class sessions is not required, I encourage you to take advantage of our time together by engaging fully with the material and with your classmates during class. Whether you miss class for a legitimate reason or simply need a day off, it is your responsibility to know and to understand the material covered that day. Ask a classmate for a copy of his or her notes and visit the course website for copies of any handouts. Please note that we cannot take class time to review material for students who miss class.