Python is a programming language that has found widespread adoption in data science, machine learning, and web-development applications while valuing code-readability. This course provides a two-day introduction to core-features that Python offers to social scientists. The course will focus on the practical use of Python by teaching participants how to scrape websites, train machine-learning algorithms, and process text in an automated fashion. The course aims to provide hands-on examples that participants can then adapt to their own needs and research interests.
Participants will have to fulfill the following requirements:
- Basic programming experience, either in Python or in another programming language (R, Stata, or others).
- Participate on each day of the workshop.
- Bring a laptop, ideally with a Python runtime installed.
- Submit a final programming assignment.
Participants can include the workshop in their grade transcript. Grades for the workshop will be based on the final programming assignment.
You will need a Python runtime environment to run Python code. You can either install it manually from the Python website or you can use a ready-made distribution of Python. In the following steps we will install Anaconda, a Python distribution. The advantage of this is that it is very simple to install and it comes with a very useful interface called Jupyter Lab.
- Visit the website of Anaconda. Select your operating system, look for Anaconda in the Python 3.11 Version, download and install it.
- After you have completed the installation, search in your Applications for
Anaconda Navigatorand start it. In the tabHome, look forJupyter Laband launch it. - If everything worked out, you can now access your own Jupyter Lab via you web browser. By default, Anaconda will automatically open a tab with the corresponding page, otherwise it can be reached in most cases via this link.
In order to access the code for this workshop, we will be using the code hosting platform GitHub. Besides access to code, this platform has a wide range of features, including consistent version control and change tracking. There are several ways to access a GitHub repository. On the one hand, there is the command line where you use commands to perform the desired actions. On the other hand, there is GitHub Desktop which allows for most actions within a graphical user interface. The latter is often a relief, especially for beginners.
- Visit the website of GitHub Desktop. Select your operating system, download and install it.
- After you have completed the installation, search in your Applications for
GitHub Desktopand start it. If you already have a GitHub account, you can sign in with it, but it is not necessary to sign in to follow the next steps. - To access the code for this workshop, click on
Clone a Repository,URL, enter asRepository URLhttps://github.com/stefan-scholz/python-workshop-2023and select asLocal Paththe folder where you want to save the materials on your local computer. If your clone worked out, you can now see the files in your local path. These files should be the same as the ones online when you open up the website via your web browser.
- Python basics and programming fundamentals (notebook)
- Working with structured data (notebook)
- Using application programming interfaces (notebook)
To get a first experience on how to use Python, here are references to introductory Python courses and books.
- Coursera/Google "Crash Course on Python"
- Coursera/IBM "Python for Data Science and AI"
- Anand Chitipothu "Python Practice Book"
- "The (Official) Python Tutorial"
- Kushal Das "Welcome to Python for you and me"
To get a first experience on how to use Anaconda, Jupyter Lab and GitHub Desktop, we recommend you to take a look at the following resources.
- Sebastian Nagel has already held this course in 2021 and 2022. He has kindly given me permission to use his materials, to which I have added more.
- Apache 2.0 for the Python code and documentation
- Listing of data sources and licenses for data shared in this repository