Module 2 – Developing digital content

Data is a new fuel and the demand on skills to tackle the data is rising. How can data help to tell the story and what are the key steps to approach the process of dataset creation?

 Data literacy is the ability to peruse, comprehend, make, and impart information as data. Similar to proficiency as an overall idea, data literacy centres around the skills associated with working with information. It is, be that as it may, not like the capacity to analyse text since it requires certain abilities including extracting and handling information.

 As data collection becomes one of the main information sources it turns out to be increasingly more significant for researchers to have data literacy skills. The core concepts are linked with data science, which tackles data collection, data collection and data interpretation and data visualization. 

 Data literacy includes understanding what does data really mean, including the capacity to understand diagrams and outlines and to drive insights from information.

 Researchers can either initiate data collection (via survey) or use existing data sources (Open Datasets). While building their own data set the researcher makes a new set of knowledge to explore the story. Unique datasets can even assist the researcher to identify what trends consultants and policy manufacturers haven’t been ready to spot. Data becomes a supply of knowledge. At the point of applying a data driven approach to the content creation, the well-structured procedure should be respected not to generate irrelevant data.

In order to handle the data one should be familiar with statistics basics and understand the topic. Sometimes data can play a bad joke and mislead the researcher. It might happen if the survey sample doesn’t represent the whole population and is biased. Sometimes respondents don’t pay enough attention to the questions and select other options by mistake. These issues can be avoided if the researcher is aware that digital data is biased and one should make sure that the sample is not biased and the survey is well compiled.


By the end of this topic, you will be able to:

  • create online forms
  • understand data capture, basic rules on validity, accuracy, quality
  • process and visualize the collected data

 The team is expected to be able to highlight information needs, to search for data in digital environments, to access the data and to navigate between datasets. The researcher needs to analyse, interpret and critically evaluate the possessed data, information and digital content.


Explorer: No prerequisites are needed except from critical thinking
Expert: The knowledge on specific vocabulary ( ex: data wrangling )


Explorer level

Data is able to share a story


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Expert level

Take best out of the data


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