Data is all of that, and much, much more. <\/strong>One of the broader definitions, coins it as \u201dinformation in digital form that can be transmitted or processed\u201d. What you should take away from that, is an open mindset that allows you to see and find data in places where others might not suspect it.<\/strong> <\/p>\n\n\n\n
The number of certain terms or phrases in a politician’s speech is data. The occurrence of extreme weather phenomenons or the occurrence of accidents in a city – all that is data. It might not be accessible as a neat Excel sheet straight away, but that does not make it less valuable as a source of information for your story. <\/p>\n\n\n\n
Where to Find Story Ideas and Data Sources? <\/h2>\n\n\n\n
The most straightforward source for data are press releases from companies or government officials, as well as publicly accessible databases. When such information is released, ask yourself what information might be provided beyond the obvious. Can you gain new and newsworthy insights by relating the provided data to other information? <\/p>\n\n\n\n
When you gather data that is available but not yet collected in a form that can be analyzed and you change that, you also gain new information. <\/p>\n\n\n\n
Other sources for data and data stories include leaks and direkt informants as well as current news stories that might be given more depth when their data-background is explored. <\/p>\n\n\n\n
You can find a great long-read on how to find stories as well as data sources under the following link.<\/a> <\/p>\n\n\n\n Sometimes the data that you are looking for is not available. In such cases thinking about data, that is a good enough substitute for the missing information, might be the solution. For example, if you are looking for the income structure of city districts and such information is not available, the average rent prices might be an acceptable substitute. <\/p>\n\n\n\n What sometimes occurs when journalists and news outlets first start with data journalism, is the use of data \u201cjust for show\u201d. Time consuming data analysis is paired with fancy visualizations for stories that are either not that relevant or don\u2019t benefit from the information that the data provides. Impact wise, this is a lot of work for very little bang. When you approach a data story, ask yourself, if it really is a data story:<\/strong> Is the data necessary to tell the story? Does the inclusion of graphs and visualizations make the story better? Does the data provide new information? If you can\u2019t answer at least one of these questions with yes, your story is most likely not a data story.<\/p>\n\n\n\n You don\u2019t have to be an expert in statistics or learn coding right away to analyze data. But there are some things you should make yourself familiar with, before you approach your first data project. <\/p>\n\n\n\n More often than not data-analysis is related to some kind of frequency concept. Making yourself familiar with some basic numerical terms will help you understand what kind of questions you can answer with your data and what stories can be realized with the help of data analysis. <\/p>\n\n\n\n Frequency per analytical entity\/density:<\/em> <\/strong><\/p>\n\n\n\n How often does a piece of data occur in your analytical entity? This is an important question since it opens up the possibility to better compare numbers with each other. For example, if you are looking into the state of medical care in your city, an interesting measure of frequency might be the number of doctors per city district.<\/p>\n\n\n\n Mean: <\/strong><\/em><\/p>\n\n\n\n The mean is what most people understand as the average. It is the number you obtain, by dividing a sum of values through the number of values.<\/p>\n\n\n\n If we have five city districts with 5, 9, 15, 25 and 27 doctors respectively, this is how you would calculate the mean: <\/p>\n\n\n\n (5+9+15+25+27)\/5 = 16,2<\/em><\/p>\n\n\n\n The mean of doctors per city district is the division of the sum of doctors from each city district (most likely the number of doctors in the city) through the number of city districts. You can use the mean for example, to evaluate whether the number of doctors in a city district is unusually high or low. <\/p>\n\n\n\n Median: <\/strong><\/em><\/p>\n\n\n\n The median, quite literally, is the number in the middle. It is not the average across a group, but rather in the middle, compared to all the other values you have. If you line up your data values from largest to smallest, the number in the center is the median. Sticking to our example, if the number of doctors in a city’s district is 5, 9, 15, 25, 27, then the median would be 15 – the number in the middle. <\/p>\n\n\n\n Mode:<\/strong><\/em> <\/p>\n\n\n\n The mode is the element that appears most frequently in a given set of elements. Sticking to the doctors example, the mode could be the most frequently occurring number of doctors per city district. If most city districts have eleven doctors, that would be the mode. It is another number you can use for comparative purposes. In any group it describes the most popular (aka. most frequently appearing) element. <\/p>\n\n\n\n While these concepts are important, the mathematical foundation that you will need, if you want to stick to data journalism long-term, is a little broader. You should also look into percentages and basic statistical concepts. <\/p>\n\n\n\n Here are a few resources to get you started: <\/p>\n\n\n\n The examples under point one have shown that Data-stories often deal with more than one source or kind of data. For the example story the main source of data and the main concept of interest is the number of doctors in a city. But to actually gain useful information, comparative data is needed<\/strong>. That could be the number and size of city districts, demographic data like income distribution or maybe the average population age in each city district. Good data stories require critical thinking about this comparative data. <\/strong>What kind of data is available, what kind of comparisons make sense and are the different kinds of data in a comparable format? <\/p>\n\n\n\n Sticking to our example, you could determine how the number of doctors per city district is connected to people’s income. In this case income would be the comparative data of your choice. You could then answer the question, whether the number of doctors is higher in richer or in poorer parts of the city. <\/p>\n\n\n\n![\"\"](\"https:\/\/www.mojo-manual.org\/wp-content\/uploads\/Data-journalism-ideas.jpg\")
<\/figure>\n\n\n\nSubstitute Data: <\/h3>\n\n\n\n
What is Useful Data?<\/h3>\n\n\n\n
Basic Skills for Data Journalists<\/h2>\n\n\n\n
1. Math for Data Journalists <\/h3>\n\n\n\n
2. Comparative Data <\/h3>\n\n\n\n