Courses for QSS minors

The QSS minor program establishes or enhances a student's statistical and computational skillset while pursuing another major program.

Please note, students must meet the minimum GPA requirement of 2.0 to graduate with any major or minor from the department.

All classes counting toward the degree must be taken for a letter grade.

 

 

QTM 100

QTM 110: Introduction to Scientific Methods

This course is designed to introduce students to the style of analytic thinking required for research in the sciences and the concepts and procedures used in the conduct of empirical research. In short, this course teaches a set of skills that are essential for both understanding the research you will encounter in substantive classes, and being able to produce high-quality original research of your own. Beyond simply learning how to be a more critical participant in the academic research community, you will also be better-prepared for career opportunities using statistical tools and the products thereof.

Students will be introduced to the basic toolkit of researchers which includes sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity. More importantly, students will learn the principles of critical thinking essential for careful and credible research.

Prerequisites: None

GER: None

Credits: 3

Offering schedule: Fall, Spring

QTM 150: Intro to Statistical Computing I

This course provides an introduction to statistical computational tools for analyzing data. The material is selected to enable you to become proficient enough to actively implement the methods and tools in your scientific research. This will require you to practice the material outside of class.

By the end of the course, students should be able to 1) deal with complex and messy real data, 2) use graphics to explore and understand data, 3) gain familiarity with basic data collections, storage, and manipulation, and 4) fluently reshape data into the most convenient form for analysis or reporting. 

Prerequisites: None

GER: None

Credits: 1

Offering schedule: Fall, Spring

QTM 151: Intro to Statistical Computing II

This course provides a practicum of skills for data science and an introduction to how to do data science with R. The material is selected to enable you to get data into the most useful structure, transform it, visualize it, and model it. This will require you to practice the material outside of class.

By the end of the course, students should be able to (1) deal with complex and messy real data (2) use graphics to explore and understand data (3) gain familiarity with basic data manipulation, (4) fluently reshape data into the most convenient form for analysis, and (5) automate cleaning and analysis.

Prerequisites: QTM 150

GER: None

Credits: 1

Offering schedule: Fall, Spring

QTM 200: Applied Regression Analysis

This course is a follow-up to QTM 100. Students will apply many of the concepts and skills learned in QTM 100 to a broader field of statistical analysis: multivariable analysis and model building.

General topics covered include linear regression and logistic regression. Regression analysis with focus on applications; examining data and transforming data; simple and multiple regression models, dummy variable regression; one-way and two-way ANOVAs; of unusual and influential data, non-linearity, non-constant variance, non-normality and collinearity; model selection; logistic regression; interpretation of analysis results; hands-on data analysis with computer software. At the end of the semester, students are expected to acquire a strong foundation in basis methods required for data analysis and interpretation.

While regression analysis does involve math, this course will not require you to memorize formulas. Rather, this course focuses on implementation of appropriate statistical methods and interpretation of results. Consequently, logical reasoning, critical thinking, and writing are also skills that will be emphasized throughout the course.

Prerequisites: QTM 100

GER: MQR

Credits: 4

Offering schedule: Spring

QTM 250: Foundations of Data Science Computing

Foundations of data science using the programming language Python. It teaches critical skills and concepts in computer programming and inference, focusing on techniques for inference that are not found in introductory statistics courses (e.g. clustering and simple neural networks). Practice is gained via hands-on analysis of real-world data sets and projects with a creative focus: students are encouraged to explore their own domain of interest from the perspective of the new tools and methods introduced in this class.

The first half of the course focuses on foundational Python and data science concepts as implemented in Python. The second half on useful packages for data science, SQL, data science on the cloud using Google's cloud platform, and elementary neural networks.

By the end of the course, each student will 1) be able to competently discuss concepts in computing that were discussed in lecture; 2) become comfortable working from the command line and using the command line to automate workflow (e.g. write a bash command replacing all the semi-colons in a text file with commas); 3)learn basic syntax and concepts for languages such as Python, SQL, Bash and certain high-level machine learning libraries which make coding a neural network for a regression problem as simple as three or four commands; 4) creatively implement what they have been learning in projects.

Prerequisites: QTM 151

GER: MQR

Credits: 3

Offering schedule: Spring

Two QTM Upper-Level Electives

Minor students can take any 300- or 400- level seminar or lecture style QTM elective that do not have QSS major core courses as prerequisites. What does this mean exactly? Any QTM elective course that lists QTM 120/210/220 (these are QSS major courses) as prerequisites is not avilable to QSS minors.

We offer a number of QTM upper-level electives that QSS minors can take. These include, but are not limited to, Technical Writing, Game Theory, Practical Approaches to Data Science with Text, Introductory Network Analysis, Fundamentals of Cartography and GIS, and Big/Small Data & Visualization, and Social Choice & Elections.