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  • Neural Data Science in Python

About This Course

  • Start with why
  • Learning Objectives for this Course
  • Syllabus
  • How to Approach this Course
    • Actually Write Code
    • Mindset
    • Flipped Classroom
    • Errors and Debugging
    • Artificial Intelligence-Assisted Coding
    • Collaboration and Teamwork
  • Pedagogy
    • Teaching Approach
    • Authenticity
    • 21st Century Skills
    • Constructivism
    • Connectivism
  • Open Resources
    • Free Software
    • Free and Open-Source Software Licenses
    • Open Science
    • Peer Review
    • For-Profit Publishing
    • Impact Factors
    • Novelty
    • Open Publishing
    • Preregistration
    • Open Methods and Data
    • Open Educational Resources
    • OERs in the Context of this Course
    • Licenses

Introduction to Data Science

  • Getting Started with the Course
  • Learning Objectives
  • What is Data Science?
    • What is Data?
    • Where Does Neural Data Come From?
    • Data Science
    • Data Cleaning
  • Tools for Neural Data Science
    • Spreadsheets
    • Limitations of Spreadsheets
    • Reproducibility
    • Scientific Programming Languages
    • Which language to use?
    • Libraries and packages
    • Why Python for this Course?
  • Coding Tools
    • Python
    • The Terminal and Command Line
    • Jupyter
    • Markdown
    • Visual Studio Code
    • GitHub

Set Up Your Computer for Data Science

  • Some Assembly Required
  • Learning Objectives
  • First Steps
    • Get on GitHub
    • GitHub Codespaces
    • GitHub Classroom
  • Install on Your Computer
    • Anaconda
    • Miniconda
    • VS Code
    • GitHub Desktop
  • Your First GitHub Repository
    • Clone a Repository from GitHub
    • Exploring the GitHub Repository view
    • Open the Repository in VS Code
    • Editing, Pushing, and Committing
    • Edit the README File
    • Create a New Markdown File
  • Getting Help on an Assignment
  • Submitting Assignments

Introducing Python

  • Introducing Python
  • Learning Objectives
  • Preliminaries
    • Working with Jupyter Notebooks
    • Getting Help with Python
  • Data types
    • Variables and Assignment
    • Data Types and Conversion
    • Python Built-Ins
    • Lists
    • Dictionaries
  • Flow Control
    • for Loops
    • Conditionals
  • Working with Data
    • pandas DataFrames
    • Looping Over Data Files

AI-Assisted Coding

  • Introducing GitHub Copilot
  • Introduction to GitHub Copilot
  • Exercises – Reaction Time Data
  • Data Files and pandas DataFrames
  • Working With Multiple Data Files Using Copilot
    • Reading Data Files with pandas
    • Computing Mean Reaction Times and Confidence Intervals
    • Formatting the Output in a Table

Visualizing Data

  • Introduction to Data Visualization
  • Learning Objectives
  • Introduction to Plotting with Matplotlib
  • Procedural versus Object-Oriented Plotting in Matplotlib
  • Subplots
  • Thinking About Data for Plotting
  • Data Science Plots with Seaborn
  • Accessibility and Human Factors in Plotting

Exploratory Data Analysis

  • Introduction to EDA
  • Learning Objectives
  • Working with Repeated Measures Data
  • Data Cleaning - Dealing with Outliers
  • Basic Statistics in Python: t tests with SciPy

Single Unit Data

  • Introduction to Single Unit Data
  • Learning Objectives
  • Single Unit Data and Spike Trains
    • Introduction to Spike Train Data
    • Effects of Light Intensity on Spike Rate
    • Heat Maps
  • Introducing Multielectrode Data
    • Working with Multielectrode Data in pandas
    • Correlating spike trains

EEG Data

  • Introduction to EEG
  • Learning Objectives
  • What is EEG?
  • EEG in the Time and Frequency Domains
    • Time and Frequency Domains
    • Event-Related Potentials (ERPs)
    • ERP Components
  • MNE-Python
  • EEG-ERP Preprocessing
    • Filtering EEG Data
    • Artifacts in EEG Data
    • Segmentation into ERP epochs
    • Averaging and Re-Referencing ERPs
  • Group Analysis of ERP Data
    • Grand Averages and Visualization
    • Statistical Analysis of ERP Data

MRI Data

  • Introduction to MRI Data
  • Learning Objectives
  • Reading and Visualizing Structural MRI Data
  • Working with NIfTI images

Machine Learning

  • Introduction

References

  • References
  • Repository
  • Open issue

Index

By Aaron J Newman

© Copyright 2020-23. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.