L&S 88 Data Science for Cognitive Neuroscience

A Fall 2017 Data Science Connector Course

CCN:46821 | Michael Eickenberg, Samy Abdel-Ghaffar | Monday 4:00-6:00 PM | 105 Cory Hall | Units: 2


Welcome to Data Science for Cognitive Neuroscience! This page will contain links to course materials and to the bCourses website. The latest syllabus can be found on the bCourses website.

  • Install the latest neurods package [Check back later]
  • A Description of Our Datasets [Check back later]
  • bCourses site
Date Module Lecture Slides/Notebooks Homework Notes
8/28 Lecture 01: Introduction to Cognitive Neuroscience Slides01 No HW this week Notepad01_Postclass
9/4 No Class - Holiday
9/11 Lecture 02: Intro to fMRI data & data types in Python Lecture 1 Review
Python Notebook02 [Your Version]
Python Notebook02 [Instructor Version]
HW #1
HW #1 Solutions
9/18 Lecture 03: Manipulating one-dimensional arrays Python Notebook03 [Your Version]
Python Notebook03 [Instructor Version]
HW #2
HW #2 Solutions
9/25 Lecture 04: Data formats and Visualizing fMRI data in 2D and 3D Python Notebook04 [Your Version]
Python Notebook04 [Instructor Version]
HW #3
HW #3 Solutions
10/02 Lecture 05: Masking and Visualizing fMRI Data in 3D Python Notebook05 [Your Version]
Python Notebook05 [Instructor Version]
HW #4
HW #4 Solutions
Notepad05 (Empty)
10/09 Lecture 06: Data Preprocessing Python Notebook06 [Your Version]
Python Notebook06 [Instructor Version]
HW #5
HW #5 Solution
10/16 Midterm None Midterm_Exam
10/23 Lecture 07: fMRI Experiments: Block Design Python Notebook07 [Your Version]
Python Notebook07 [Instructor Version]
HW #6
HW #6 Solutions
10/30 Lecture 08: fMRI Experiments: Block Design Python Notebook08 [Your Version]
Python Notebook08 [Instructor Version]
HW #7
HW #7 Solutions
11/06 Lecture 09: Correlation and Regression in fMRI Python Notebook09 [Your Version]
Python Notebook09 [Instructor Version]
HW #8
HW #8 Solutions
11/13 Lecture 10: Multiple Regression and Hypothesis Testing Python Notebook10 [Your Version]
Python Notebook10 [Instructor Version]
HW #9
HW #9 Solutions
11/20 Lecture 11: Hypothesis Testing, Contrasts and Multiple Comparison Correction Python Notebook11 [Your Version]
Python Notebook11 [Instructor Version]
No Homework Notepad11_Postclass
11/27 Lecture 12: Bootstrapping, Multiple Comparison Correction, and Model Prediction Python Notebook12 [Your Version]
Python Notebook12 [Instructor Version]
HW #10
HW #10 Solutions
Lab#2 Solutions
12/15 Final Exam Final Exam

Course Description

The human brain is a complex information processing system and is currently the topic of multiple fascinating branches of research. Understanding how it works is a very challenging scientific task. In recent decades, multiple techniques for imaging the activity of the brain at work have been invented, which has allowed the field of cognitive neuroscience to flourish. Cognitive neuroscience is concerned with studying the neural mechanisms underlying various aspects of cognition, by relating the activity in the brain to the tasks being performed by it. This typically requires exciting collaborations with other disciplines (e.g. psychology, biology, physics, computer science).

You should take this course if you’re interested in how the brain works and how you can use cutting edge brain imaging and data analysis tools to study it. During this course, you will learn tools based on the python programming language to understand, manipulate, and explore human brain recordings (fMRI). You will learn to formulate hypotheses about how the brain represents information and then test these hypotheses using real world data. You will learn useful analysis methods to help you derive conclusions from brain recording data.

By giving you first hand experience in data analysis of brain data, this course will provide you an insight into the experiments and data used in the cognitive neuroscience field. It will allow you to build a better understanding of the current cutting edge research in cognitive neuroscience. Hence, you will be able to keep up with recent advances in this field and/or will be able to apply your knowledge by doing research here at Berkeley. Additionally, the data analysis techniques and the investigation approaches that you will learn will be easily transferable to research in other disciplines.

Contact Information

Instructors:Michael Eickenberg, Samy Abdel-Ghaffar


Michael: michael.eickenberg@berkeley.edu, Samy: samyag1@berkeley.edu

Office Hours

Tuesday 11am-12pm


Evans Hall B6