| Date | Lecture | Lab |
|---|---|---|
| Wed, Sept 4 | Introductions, course policies | [HW1: Pre-class Survey] - Due Sun Sept 8th |
| Mon, Sept 9 | What is cognitive science? Introduction to Jupyter [Lecture 1] [Chapter 1] - read before next class |
Accessing JupyterHub, Basics of Jupyter, python, and markdown |
| Wed, Sept 11 | What is an experiment? How does science use experiments to advance knowledge? [Lecture 2] [Chapter 2] - read before next class |
intro to Python: Numbers, variables [Lab: Python Intro] |
| Mon, Sept 16 | Why do we do statistics? -- [Lecture 2 continued] [Chapter 3] - read before next class |
Indenting, using existing functions, importing libraries |
| Wed, Sept 18 | Basic experiment Design: Randomization, stratification, counter-balancing, factorial designs [Lecture 3] |
Lists and dictionaries, Loops, and other control structures |
| Mon, Sept 23 | Measures of central tendency, variance [Lecture 4] [Chapter 4] - read before next class [Podcast] - listen before class |
Functions and organizing code blocks [Lab: DataFrames and plotting] |
| Wed, Sept 25 | No lecture | Reading in data and manipulating it [HW2: Intro to Jupyter] |
| Mon, Sept 30 | Random processes, probabilities, distributions, and sampling [Lecture 5] [Chapter 5.1] - read before next class |
Tidy data formats and preparing data for analysis |
| Wed, Oct 2 | Exploratory Data Analysis [Chapter 5.1] - read before next class |
Visualizing data with Seaborn [Lab: Exploring Data] |
| Mon, Oct 7 | Sampling and Estimation [Lecture 5 (Cont)] [Chapter 5.2] - read before next class |
Accessing existing data sets [HW3: Python for Data Analysis] |
| Wed, Oct 9 | Relating multiple variables (correlation) [Lecture 6] [Chapter 6] - read before next class |
Catch up day |
| No class, University holiday | ||
| Tues, Oct 15th (Rescheduled class) | Regression | [Lab: Intro to Regression] |
| Wed, Oct 16 | Multiple Regression [Chapter 7] - read before next class |
[Lab: Intro to Regression] |
| Mon, Oct 21 | No lecture [Chapter 8] - read before next monday |
[Lab: Advanced Regression] |
| Wed, Oct 23 | Mental Rotation/Simulation [Lecture 7] |
[Lab: Advanced Regression] |
| Mon, Oct 28 | No lecture [Chapter 9] - read before next monday |
[Lab: Mental Rotation] |
| Wed, Oct 30 | Functional Magnetic Resonance Imaging (fMRI) part 1 [Lecture 8] [Chapter 10] - read before next monday |
[Lab: fMRI] |
| Mon, Nov 4 | Hypothesis testing and Multiple Comparisons [Chapter 11] - read before next monday |
[Lab: fMRI pt2] |
| Wed, Nov 6 | Signal Detection Theory [Lecture 9] |
[Lab 2: Signal Detection Theory] |
| No class (illness-related cancelation) [Chapter 12] - read before next monday |
- | |
| Wed, Nov 13 | Signal Detection Theory - Perception | [Lab 2: Signal Detection Theory] |
| Mon, Nov 18 | T-test review [Lecture 10] |
[Lab 2: Signal Detection Theory] [Lab 2: T-test lecture] |
| Wed, Nov 20 | Catch up day | Catch up day |
| Mon, Nov 25 | Computational modeling, intro to RL, final projects | [Lab 3: Reinforcement Learning] |
| No class, Thanksgiving break | ||
| Mon, Dec 2 | Literature reviews, findings papers online, reference managers and citations [Chapter 13] - read before next wednesday |
Final projects |
| Wed, Dec 4 | Work on final projects in class | Final projects |
| Mon, Dec 9 | Replication crisis, HARKing, p-hacking, blinding, preregistration, open science | [Lab 3: Reinforcement Learning] Final projects |
| Wed, Dec 11 | Informed consent, institutional review boards, ethics in research, Giving Presentations | |
| Mon, Dec 16 from 10-11:50am | Final oral presentation, final papers due |