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Acknowledgments

1. About This Course

About this course
Learning Objectives for this Course
Academic Integrity and Collaboration

2. Introduction to Data Science

Getting Started with the Course
Learning Objectives
What is Data Science?
- Data
Tools for (Kinesiological) Data Science
Coding Tools
- Python
- DataCamp
- Jupyter
- Markdown

3. Introduction to Python

Introducing Python
Learning Objectives
Introduction to Jupyter Notebooks in JupyterHub
Getting Help with Python
Data types
Flow Control
- for Loops
- Conditionals
Writing Functions
Working with Data
- Introduction to pandas DataFrames
- Looping Over Data Files

4. Data Visualization

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

5. 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

6. Statistical Inference and the Bootstrap

Statistical inference

7. Single Unit Data

Introduction to Single Unit Data
Learning Objectives
Single Unit Data and Spike Trains
Introducing Multielectrode Data
- Working with Multielectrode Data in pandas
- Correlating spike trains

8. Signal Processing

Signals, Sampling & Filtering

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By Hyosub Kim

© Copyright 2023. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License..