Big Data in Contemporary Neurobiology (368-0-20)

Instructors

Andrew Miri

David Xing

Meeting Info

Annenberg Hall G30: Tues, Thurs 2:00PM - 3:20PM

Overview of class

The increasing ease of collecting big datasets and of computational analysis is transforming the way science is done. Quantitative methods beyond those covered in a first course on scientific statistics are now commonly applied in contemporary neuroscience literature. Engaging with this literature increasingly requires a basic understanding of these methods and how their results should, and should not, be interpreted.
This class is intended to help students build this basic understanding. The course is accessible to students who have not taken mathematics or data science courses beyond the requirements for the Neuroscience Major. The focus is not on applying methods, but rather on understanding their application in the literature. Topics include linear and nonlinear dimensionality reduction, clustering, classification, predictive modeling, and p-value interpretation. Class meetings will be half chalktalk about methods, and half discussion about literature readings. Assessment will include problem sets, class participation, in-class exams, and a final project. Students must have met the statistics course requirement for the Neuroscience Major and taken either: NEUROSCI 202-0 and NEUROSCI 2006-0; or NEUROSCI 311-0 and NEUROSCI 2006-0; or BIOL_SCI 302-0.

Registration Requirements

Prerequisites: BIOL_SCI 337-0, BMD_ENG 220-0, CSD 304-0, ES-APPM 375-1, IEMS 201-0, PSYCH 201-0, STAT 202-0, STAT 210-0, or STAT 383-0 and either: NEUROSCI 202-0 and NEUROSCI 206-0; or NEUROSCI 311-0 and NEUROSCI 206-0; or BIOL_SCI 302-0.

Learning Objectives

1. To build a conceptual understanding of data analysis methods now commonly applied in the neurobiology literature (primary assessment: problem sets, exams, final paper)
2. To build skills for understanding the application of these methods in the literature (primary assessment: discussion participation, final paper)
3. To develop the ability to figure out new methods you encounter in the literature (primary assessment: discussion participation, final paper)

Teaching Method

Two, 80-minute meetings per week. Class meetings will be half chalktalk about methods, and half discussion about literature readings.

Evaluation Method

Graduate students:
one of the primary research papers will be read in its entirety, and students will write a 250-word response based on it.
completing the 250-word responses will factor into your class participation grade
For the Final project, the proposal must be 1600 words.

Class Materials (Required)

Something to write on in class for doing problems (tablet or actual paper).

Class Materials (Suggested)

none

Class Notes

We want to help all of you learn and expect that all of you can succeed! You will not be graded in comparison to other students, there is no curve, and there are no limits on the number of students receiving any particular grade. Your overall grade in the course will be based on a percentage of the points earned across all assignments. You are expected to attend all class meetings.

FINAL EXAM WILL BE HELD DURING FINALS WEEK.

Class Attributes

SoC Mathematics, Science, and Technology Distro

Enrollment Requirements

Enrollment Requirements: Prerequisites: BIOL_SCI 337-0, BMD_ENG 220-0, CSD 304-0, ES-APPM 375-1, IEMS 201-0, PSYCH 201-0, STAT 202-0, STAT 210-0, or STAT 383-0 and either: NEUROSCI 202-0 and NEUROSCI 206-0; or NEUROSCI 311-0 and NEUROSCI 206-0; or BIOL_SCI 302-0.