This seminar will support your transition to college and introduce you to a topic in public health and health sciences. The first six weeks will provide foundational skills for students to thrive at UMass. The last seven weeks will focus on exploring the topic: The Opioid Crisis. This seminar is designed to give a historical overview of The Opioid Crisis. We will utilize public health and other related academic disciplines to think critically about The Opioid Crisis, and to answer how we got here, where we stand, and where we are headed.

(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology.

The practice of data science rests upon computing environments that foster responsible uses of data and reproducible scientific inquiries. This course develops students’ ability to engage in data science work using modern workflows, open-source tools, and ethical practices. Students will learn how to author a scientific report written in a lightweight markup language (e.g., markdown) that includes code (e.g., R), data, graphics, text, and other media. Students will also learn to reason about ethical practices in data science.

The practice of data science rests upon computing environments that foster responsible uses of data and reproducible scientific inquiries. This course develops students’ ability to engage in data science work using modern workflows, open-source tools, and ethical practices. Students will learn how to author a scientific report written in a lightweight markup language (e.g., markdown) that includes code (e.g., R), data, graphics, text, and other media. Students will also learn to reason about ethical practices in data science.

The practice of data science rests upon computing environments that foster responsible uses of data and reproducible scientific inquiries. This course develops students’ ability to engage in data science work using modern workflows, open-source tools, and ethical practices. Students will learn how to author a scientific report written in a lightweight markup language (e.g., markdown) that includes code (e.g., R), data, graphics, text, and other media. Students will also learn to reason about ethical practices in data science.

In this course, we will examine how narrators and narration drive and impose structure onto short stories. By doing so, we will begin to consider the role of the narrator in our own creative work. We will study the role narrators play in the function of the stories they tell, whether they feature in those stories or not.