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The following provides links to the Jupyter Notebook files (ipynb) used for the workshop as well as web document version of the files for viewing. Files will be added as the lessons progress. The contents of this workshop are licensed under Creative Commons Attribution 4.0 International License.

Week 1: 04/04/23 | Introduction to Anaconda, DataSpell, and its Workspace

Week 2: 04/11/23 | Working with NumPy, Pandas, Matplot, and Seaborn

Webpage: Python for Social Science Workshop - Lesson 2
Code: Python for Social Science Workshop - Lesson 2 (.ipynb)
Code: Challenge 1 (.ipynb) (Solutions)
Data: Asylum Applications in the EU (.csv)

Week 3: 04/18/23 | Univariate and Multivariate Regressions

Webpage: Python for Social Science Workshop - Lesson 3
Code: Python for Social Science Workshop - Lesson 3 (.ipynb)
Data: Datasets for Lesson (.zip)

Week 4: 04/25/23 | Webscraping and APIs

Week 5: 05/02/23 | Text Analysis

Webpage: Python for Social Science Workshop - Lesson 5
Code: Python for Social Science Workshop - Lesson 5 (.ipynb)
Data: Datasets for Lesson (.zip)

References

The following are sources that were used as guides to construct the lesson plans:

Adhikari, A. and DeNero, J. (2020). Computational and Inferential Thinking: The Foundations of Data Science. https://inferentialthinking.com/.
Duquette, Nicolas. 2022. PPD 599: Python for Public Policy and Management. USC Course.
GeeksForGeeks. Python Tutorial. https://www.geeksforgeeks.org/python-programming-language/?ref=shm
Johnsson, Ida Brigitta. 2022. ECON 570: Big Data Econometrics. USC Course.
McKinney, W. (2018). Python for Data Analysis: Data wrangling with Pandas, NumPy, and IPython.
VanderPlas, Jake. 2022. Python Data Science Handbook, 2nd Edition.

Python for Social Science Workshop