## BIOHOPK 140H: Statistical Modeling (BIOHOPK 240H)

(Graduate students register for 240H.) Introduction to applied statistical modeling in a Bayesian framework. Topics will include probability, regression, model comparison, and hierarchical modeling. We will take a hands-on, computational approach (R, Stan) to gain intuition so that students can later design their own inferential models. Prerequisites for this course include introductory statistics and some calculus or linear algebra, as well as previous exposure to scientific computing. Open to graduate students; undergraduate students may enroll with consent of instructor.

Terms: Win
| Units: 3

Instructors:
Elahi, R. (PI)

## BIOHOPK 157H: Creative Writing & Science: The Artful Interpreter (BIOHOPK 257H, ENGLISH 157H)

What role does creativity play in the life of a scientist? How has science inspired great literature? How do you write accessibly and expressively about things like whales, DNA or cancer? This course meets on main campus and begins with a field trip to Hopkins Marine Station, perched at the edge of the Pacific, where Stanford labs buzz with activity alongside barking seals and crashing waves. Here, in this spectacular setting, we learn to pay attention to our encounters with the natural world and translate sensory experience to the page. Students keep field journals to collect observations and cultivate a reflective practice. In-class writing experiments lead to original nonfiction combining personal narrative and scientific curiosity. Students workshop their projects, receiving supportive feedback from the group. You will develop a more patient and observant eye, improve your ability to articulate scientific concepts, and, hopefully, have a bit of fun along the way.nNOTE: First priority to undergrads. Students must attend the first class meeting to retain their roster spot.

Terms: Win
| Units: 5
| UG Reqs: WAY-A-II, WAY-CE

Instructors:
Michas-Martin, S. (PI)

## BIOHOPK 161H: Invertebrate Zoology (BIOHOPK 261H)

(Graduate students register for 261H.) Survey of invertebrate diversity emphasizing form and function in a phylogenetic framework. Morphological diversity, life histories, physiology, and ecology of the major invertebrate groups, concentrating on local marine forms as examples. Current views on the phylogenetic relationships and evolution of the invertebrates. Lectures, lab, plus field trips. Satisfies Central Menu Area 3 for Bio majors.

Terms: Win
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-SMA

## BIOHOPK 174H: Experimental Design and Probability (BIOHOPK 274H)

(Graduate students register for 274H.) Variability is an integral part of biology. Introduction to probability and its use in designing experiments to address biological problems. Focus is on experimental design and the use of linear models in testing hypotheses (e.g., analysis of variance, regression). Students will use R to explore and analyze locally relevant biological datasets. No programming or statistical background is assumed. Prerequisite: consent of instructor.

Terms: Spr
| Units: 3
| UG Reqs: GER: DB-NatSci, GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Elahi, R. (PI)

## BIOHOPK 175H: Marine Science and Conservation in a Changing World (BIOHOPK 275H)

Graduate students register for 275H. This hands-on, experiential course provides a broad foundation in marine science, and explores emerging opportunities for innovation in the study of life in the sea. Students are resident at Stanford¿s Hopkins Marine Station in Pacific Grove (90 miles south of main campus) where the diverse organisms and environments of Monterey Bay provide the focus for the course. Class meets daily with lectures, discussions, labs, and field work throughout the day. Three linked concentrations¿each 3 weeks long¿are taught sequentially to address (1) the extraordinary diversity of marine organisms and habitats, (2) the physiology and behavior of marine animals, and (3) the principles of marine ecology. Connecting these concentrations is a weekly seminar-based discussion of topics in marine conservation. This design permits deep concentration on each subject, and places emphasis on discussion, group dialog, individual exploration, and experiential learning. In the final week of the quarter, students complete an individual capstone project of their choosing. This course fulfills the same laboratory requirement as
BIO 47.

Terms: Spr
| Units: 16

Instructors:
Crowder, L. (PI)
;
De Leo, G. (PI)
;
Denny, M. (PI)
...
more instructors for BIOHOPK 175H »

Instructors:
Crowder, L. (PI)
;
De Leo, G. (PI)
;
Denny, M. (PI)
;
Elahi, R. (PI)
;
Gilly, W. (PI)
;
Goldbogen, J. (PI)
;
Micheli, F. (PI)
;
Thompson, S. (PI)

## BIOHOPK 198H: Directed Instruction or Reading

May be taken as a prelude to research and may also involve participation in a lab or research group seminar and/or library research. Credit for work arranged with out-of-department instructors restricted to Biology majors and requires department approval. May be repeated for credit. (Staff)

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable for credit

Instructors:
Block, B. (PI)
;
Crowder, L. (PI)
;
De Leo, G. (PI)
...
more instructors for BIOHOPK 198H »

Instructors:
Block, B. (PI)
;
Crowder, L. (PI)
;
De Leo, G. (PI)
;
Denny, M. (PI)
;
Elahi, R. (PI)
;
Gilly, W. (PI)
;
Goldbogen, J. (PI)
;
Lowe, C. (PI)
;
Micheli, F. (PI)
;
Palumbi, S. (PI)
;
Thompson, S. (PI)
;
Watanabe, J. (PI)

## BIOHOPK 199H: Undergraduate Research

Qualified undergraduates undertake individual work in the fields listed under 300H. Arrangements must be made by consultation or correspondence.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable for credit

Instructors:
Block, B. (PI)
;
Crowder, L. (PI)
;
De Leo, G. (PI)
...
more instructors for BIOHOPK 199H »

Instructors:
Block, B. (PI)
;
Crowder, L. (PI)
;
De Leo, G. (PI)
;
Denny, M. (PI)
;
Elahi, R. (PI)
;
Gilly, W. (PI)
;
Goldbogen, J. (PI)
;
Lowe, C. (PI)
;
Micheli, F. (PI)
;
Palumbi, S. (PI)
;
Thompson, S. (PI)
;
Watanabe, J. (PI)

## BIOHOPK 240H: Statistical Modeling (BIOHOPK 140H)

(Graduate students register for 240H.) Introduction to applied statistical modeling in a Bayesian framework. Topics will include probability, regression, model comparison, and hierarchical modeling. We will take a hands-on, computational approach (R, Stan) to gain intuition so that students can later design their own inferential models. Prerequisites for this course include introductory statistics and some calculus or linear algebra, as well as previous exposure to scientific computing. Open to graduate students; undergraduate students may enroll with consent of instructor.

Terms: Win
| Units: 3

Instructors:
Elahi, R. (PI)

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