python-workshop

This tutorial is an introduction to the Python programming for psychology students without prior programming and statistics knowledge. Python comes with a simple and interpretable syntax, which makes it very easy for beginners to learn. This language is also widely used in both research and industry, from web development to data analysis to machine learning. In this tutorial, we will start from scratch to learn the basics of Python, e.g., data types and control flow, and we will also learn some powerful external libraries like Numpy, Pandas, and Matplotlib that allows efficient handling and visualizations of dataset. Each tutorial will be provided in the form of interactive coding tutorials hosted in Google Colab (which means you don’t have to install anything! Only thing you need is an access to the internet). Along the way, you will be able to accomplish programming tasks related to the basics of Python and to develop your skillset for data analysis

Chapter 1 Python Warmup

	Tutorial	Answer Key
Chapter 1

Chapter 2 Variables and Data Structure

	Tutorial	Answer Key
Chapter 2

Chapter 3 Control Logics and Functions

	Tutorial	Answer Key
Chapter 3

Chapter 4 Classes and Libraries

	Tutorial	Answer Key
Chapter 4

Chapter 5 Miscellenous

	Tutorial	Answer Key
Chapter 5

Chapter 6 Numpy

	Tutorial	Answer Key
Chapter 6

Chapter 7 Pandas

	Tutorial	Answer Key
Chapter 7

Chapter 8 Matplotlib

	Tutorial	Answer Key
Chapter 8

Additional Resources for Learning Python

https://www.coursera.org/professional-certificates/google-it-automation

Websites for Learning Python

• https://www.edx.org/professional-certificate/introduction-to-python-programming
• https://www.coursera.org/specializations/python
• https://developers.google.com/edu/python
• https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
• https://www.djmannion.net/psych_programming/index.html

Informational Video

• https://www.youtube.com/watch?v=rfscVS0vtbw

References

Tutorials ch1-5 adapted from:

• https://github.com/zhiyzuo/python-tutorial
• https://github.com/jerry-git/learn-python3

Numpy tutorials adapted from:

• Nicolas P. Rougier, . (2015, August 21). numpy-tutorial: Version 1.0 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.28817 (https://github.com/rougier/numpy-tutorial All code and material is licensed under a Creative Commons Attribution-ShareAlike 4.0.)
• Nicolas P. Rougier, ., Jessica B. Hamrick, ., ibah, ., Gavin Heverly-Coulson, ., Dapid, ., Christoph Deil, ., & Bartosz Telenczuk, . (2016, August 27). numpy-100: Version 1.1 (Version 1.1). Zenodo. http://doi.org/10.5281/zenodo.61020 (https://github.com/rougier/numpy-100 All code and material is licensed under MIT license)
• https://github.com/ksopyla/numpy-tutorial/ All code and material is licensed under MIT license. Copyright (c) 2017 Krzysztof Sopyła

Pandas tutorials adapted from:

• https://github.com/remayer/WS19_Python_for_PsychologistsBSD 3-Clause License/ Copyright (c) 2019, remayer/dominikkraft. All rights reserved.
• https://github.com/guipsamora/pandas_exercises under BSD 3-Clause License. Copyright (c) 2018, Guilherme Samora. All rights reserved.

Matplotlib tutorials adapted from:

• https://github.com/matplotlib/AnatomyOfMatplotlib Distributable under the CC-Attribution-3.0 Unported license.
• https://github.com/matplotlib/cheatsheets Distributable under the CC-Attribution-3.0 Unported license.