- Quote: "Data journalism or data-driven journalism (DDJ) is journalism based on the filtering and analysis of large data sets for the purpose of creating or elevating a news story."
Understanding the various tools and technologies available for data journalism, including statistical software, data visualization tools, and more.
Statistics: Basic statistical concepts such as mean, median, mode, standard deviation, correlation, regression analysis, and hypothesis testing.
Data Collection: Techniques for collecting data using various methods such as surveys, official government data, social media data, and text analysis.
Data Cleansing: Process of cleaning, verifying, and validating data to ensure accuracy, completeness, and consistency of the data.
Data Visualization: Techniques for creating visual representations of data such as charts, graphs, maps, and infographics.
Data Analysis: Different statistical analysis tools, such as Excel or Python, used for analyzing data as part of data journalism.
Data storytelling: An important aspect of data journalism, which involves storytelling through data visualization, and narrations.
Data Ethics: Guidelines and principles which lay out ethical considerations while working with data and sensitive information.
Data Security: Measures taken to protect data, such as encryption, access management, and proper data destruction.
Case studies: Examples of data-driven investigative journalism such as the Panama Papers or The Guardian's police killings.
Journalism ethics: Similar to data ethics, the ethics and responsibilities of journalists when reporting on data-driven stories.
Data visualization tools: These tools help in creating interactive charts, graphs, maps and other visualizations that help in presenting data in an easily understandable format.
Data scraping tools: These tools are used to extract data from online sources such as websites, social media platforms, and other online repositories.
Data cleaning tools: These tools help in cleaning and transforming raw data into more usable and structured data.
Data analysis tools: These tools help in analyzing and interpreting data to uncover insights, trends, and patterns.
Mapping tools: These tools help in creating interactive maps that can be used to visualize data geographically.
Text analysis tools: These tools help in analyzing and extracting insights from large volumes of text data.
Data management tools: These tools help in storing, managing, and organizing data in a structured manner.
Collaboration tools: These tools help in enabling collaboration among data journalists, editors, and other stakeholders working on data journalism projects.
Geo-visualization tools: These tools help in visualizing datasets on maps in order to understand the spatial patterns of the information.
Crowdsourcing tools: These tools help in engaging the public to collect and submit data for use in data journalism projects.
- Quote: "Data journalism reflects the increased role of numerical data in the production and distribution of information in the digital era."
- Quote: "It involves a blending of journalism with other fields such as data visualization, computer science, and statistics, 'an overlapping set of competencies drawn from disparate fields'."
- Quote: "Many data-driven stories begin with newly available resources such as open source software, open access publishing and open data, while others are products of public records requests or leaked materials."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "The findings from data can be transformed into any form of journalistic writing."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development."
- Quote: "This connection between data and story can be viewed as a 'new arc' trying to span the gap between developments that are relevant, but poorly understood, to a story that is verifiable, trustworthy, relevant, and easy to remember."
- Quote: "The process builds on the growing availability of open data that is freely available online and analyzed with open source tools."
- Quote: "Data-driven journalism strives to reach new levels of service for the public, helping the general public or specific groups or individuals to understand patterns and make decisions based on the findings."
- Quote: "Other labels for partially similar approaches are 'precision journalism', based on a book by Philipp Meyer, published in 1972, where he advocated the use of techniques from social sciences in researching stories."
- Quote: "Telling stories based on the data is the primary goal."
- Quote: "This approach to journalism builds on older practices, most notably computer-assisted reporting (CAR) a label used mainly in the US for decades."
- Quote: "Many data-driven stories begin with newly available resources such as open source software."
- Quote: "Data-driven journalism might help to put journalists into a role relevant for society in a new way."
- Quote: "Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development."
- Quote: "Visualizations can be used to create a clear understanding of a complex situation."
- Quote: "Many data-driven stories begin with newly available resources such as open access publishing."