This lesson provides strategies for incorporating game creation into the classroom. The first half of the lesson discusses the challenges and benefits of teaching game creation while the second half includes a technical tutorial for Twine, an open source game creation tool.
This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.
Pandas is a popular and powerful package used in Python communities for data handling and analysis. This lesson describes crowdsourcing as a form of data creation as well as how pandas can be used to prepare a crowdsourced dataset for analysis. This lesson covers managing duplicate and missing data and explains the difficulties of dealing with dates.
In this lesson you will learn about text reuse detection -- the automatic identification of reused passages in texts -- and why you might want to use it in your research. Through a detailed installation guide and two case studies, this lesson will teach you the ropes of Passim, an open source and scalable tool for text reuse detection.
This lesson covers how to convert images of text into text files and translate those text files. The lesson will also cover how to organize and edit images to make the conversion and translation of whole folders of text files easier and more accurate. The lesson concludes with a discussion of the shortcomings of automated translation and how to overcome them.
In this lesson you will be introduced to the challenges and opportunities that Jekyll, a popular, static site generator, offers for publishing collaborative, ongoing research online.
This lesson introduces three common measures for determining how similar texts are to one another: city block distance, Euclidean distance, and cosine distance. You will learn the general principles behind similarity, the different advantages of these measures, and how to calculate each of them using the SciPy Python library.
Learn how to perform OCR and text extraction with free command line tools like Tesseract and Poppler and how to get an overview of large numbers of PDF documents using topic modeling.
Jupyter notebooks provide an environment where you can freely combine human-readable narrative with computer-readable code. This lesson describes how to install the Jupyter Notebook software, how to run and create Jupyter notebook files, and contexts where Jupyter notebooks can be particularly helpful.
Clustering with Scikit-Learn in Python
