Schedule¶
For deadlines and exact dates, please consult the syllabus on canvas.
Week 1¶
1. Discussing data journalism, its practicalities, and its ethics¶
A first encounter with Jupyter Notebook
Today’s meeting will start with a theoretical overview, class discussions, and end with a brief introduction to the practicalities of this course. We expect everyone to have a working version of the software, as described in the pre-course email.
Assigned readings:
Cushion, S., Lewis, J., & Callaghan, R. (2017)
Craig, D., Ketterer, S., & Yousuf, M. (2017)
2. Lab: Finding and exploring datasets with Python & Jupyter Notebook¶
We will use different open government datasets and get acquainted with commonly used file formats such as .csv and .json. You will also learn how so-called APIs can be used to get access to online data, and how Python will allow you to get all tables from a webpage with one line of code.
Assigned readings:
From DJH: https://datajournalism.com/read/handbook/two/working-with-data/accounting-for-methods-in-data-journalism-spreadsheets-scripts-and-programming-notebooks
Salganik (2018), Chapter 2.3
Week 2¶
1. Basic statistics¶
In particular, you will be able to make easy-to-comprehend statements based on summary statistics derived from both easy and complex datasets.
Assigned reading:
St. Louis (2011)
2. Lab session: finding & accessing datasets for Step 1 of your Data Project¶
Here you have time in class to work on your Data Project. We will be on hand to help you when we can. By the end of this session, you should have found at least one dataset you can use for your final project, and be preparing to submit Step 1 (see below for assignment descriptions—note, you will need to find 2 datasets for this assignment, so use this session as productively as you can!).
European Journalism Centre’s Conversations with data: “6-ways to use APIs for journalism”
Week 3¶
1. Filtering, merging, cleaning, aggregating datasets¶
We will (continue to) discuss how to filter, merge, and aggregate datasets. How can you figure out what’s in the dataset you’ve found? How can you clean it, and perhaps join it with another dataset?
2. Lab session: train your skills¶
Here we will work together through a notebook (or two) designed to practice these data wrangling skills.
Week 4¶
1. Analyzing text, making numerical claims based on textual data¶
We will learn how to extract information from text by turning text into numbers to answer questions like: “how often does politician X mention Y?”, “Which documents are relevant for me and should I read in detail?”.
Assigned reading:
Boumans, J. W., & Trilling, D. (2016)
2. Open lab session to work on your data journalism projects¶
Recommended additional reading for a deeper practical guide to dealing with text in Python:
Trilling (2018), Chapter 7
Week 5¶
1. Visualization introduction¶
We will create insightful visualizations using matplotlib and seaborn and discuss which type of visualization is suitable for which type of data.
Assigned reading:
Segel & Heer (2010)
Kirk (2016), Chapter 1