13.2 Data Journalism and Analysis
4 min read•july 30, 2024
is revolutionizing . By combining traditional methods with , journalists can uncover complex stories and tackle systemic issues. This approach enhances credibility and objectivity, but also raises ethical considerations around and .
The integration of data analysis has led to new storytelling formats and skills. Journalists now create and , developing expertise in , , and . This allows readers to explore information at their own pace and depth.
Data in Investigative Reporting
Role and Impact of Data Journalism
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- Data journalism integrates traditional reporting methods with data analysis to uncover complex stories
- Big data and open data initiatives expand the scope of investigative reporting allowing journalists to tackle larger systemic issues (global climate change patterns, international financial corruption)
- Enhances credibility and objectivity by providing empirical evidence to support claims and narratives
- Ethical considerations include ensuring data privacy, avoiding misrepresentation, and maintaining transparency about data sources and methodologies
- crucial for modern journalists to effectively interpret, analyze, and communicate findings from complex datasets
- Collaborative efforts between data scientists, statisticians, and journalists produce high-impact investigative reports (Panama Papers investigation)
New Storytelling Formats and Skills
- Integration of data analysis led to interactive visualizations and data-driven narratives
- Journalists develop skills in data visualization, statistical analysis, and programming
- Multimedia storytelling combines text, visuals, and interactive elements to engage audiences
- Data-driven narratives allow readers to explore information at their own pace and depth
- Journalists learn to translate complex data findings into accessible language for general audiences
- New roles emerge in newsrooms such as , , and
Acquiring and Cleaning Datasets
Data Acquisition Methods
- Identify reliable sources including , , , and (FOIA) requests
- Understand data formats (, , ) and database structures for efficient collection and storage
- Develop proficiency in data manipulation tools such as , , or programming languages like or
- Implement web scraping techniques to extract data from online sources when APIs are unavailable
- Utilize to obtain non-public government data for investigative stories
- Explore methods to gather data from public contributions (citizen science projects)
Data Cleaning and Preparation
- Apply techniques including handling , removing , and standardizing formats
- Merge multiple datasets by carefully considering common identifiers and potential discrepancies in data structures
- Document data cleaning processes for transparency and reproducibility in journalistic analysis
- Assess by checking for accuracy, completeness, consistency, and timeliness of information
- Implement techniques to ensure integrity of cleaned datasets
- Develop strategies for handling and anomalies in datasets
- Create to explain variables and coding schemes used in the cleaned dataset
Data Visualization for Storytelling
Principles of Effective Data Visualization
- Transform complex information into accessible, visually appealing formats that complement written narratives
- Select appropriate chart types based on data nature and story ( for comparisons, for trends, for relationships)
- Apply principles of including clarity, accuracy, and ability to convey key insights at a glance
- Utilize color theory and typography to create impactful and accessible data visualizations
- Design visualizations for different mediums (print, web, mobile) considering their unique constraints and opportunities
- Incorporate principles of user experience (UX) design in interactive visualizations
- Balance aesthetics with functionality to create visually appealing yet informative graphics
Advanced Visualization Techniques
- Create interactive visualizations allowing readers to explore data independently, adding depth to storytelling
- Apply techniques such as mapping and choropleth maps for stories with geographic components
- Develop proficiency in visualization tools like , , or R's for creating sophisticated data graphics
- Implement animation and transition effects to illustrate changes over time or highlight key data points
- Utilize to compare multiple related datasets or variables simultaneously
- Incorporate data-driven illustrations and to explain complex concepts visually
- Experiment with emerging visualization techniques such as (VR) or (AR) for immersive data experiences
Interpreting Statistical Findings
Fundamental Statistical Concepts
- Apply including measures of (, , ) and (, ) to summarize dataset characteristics
- Utilize to make predictions or draw conclusions about larger populations based on sample data
- Recognize the difference between correlation and causation to avoid misinterpreting relationships in data
- Conduct and interpret to assess the significance of findings (, )
- Understand and when reporting statistical results
- Apply to evaluate the likelihood of events or outcomes in data analysis
- Interpret to assess the practical significance of statistical findings
Advanced Analysis and Communication
- Utilize to explore relationships between variables and identify trends in data
- Develop awareness of common statistical pitfalls such as , , and
- Apply techniques to examine complex relationships among multiple variables
- Communicate statistical findings in clear, non-technical language while maintaining accuracy and context for general audiences
- Develop skills in data storytelling to present statistical results in a compelling narrative format
- Collaborate with subject matter experts to ensure accurate interpretation of statistical findings in specialized fields
- Implement reproducible research practices to allow verification and extension of statistical analyses
Key Terms to Review (71)
Animation effects: Animation effects refer to the techniques used to create the illusion of movement and change within digital content, enhancing visual storytelling and user engagement. These effects can be applied to text, images, charts, and other data visualizations to help illustrate trends or relationships, making complex information more accessible and engaging for audiences.
APIs: APIs, or Application Programming Interfaces, are sets of rules and protocols that allow different software applications to communicate with each other. They play a crucial role in data journalism by enabling journalists to access and utilize data from various sources, enhancing their ability to analyze and present information effectively.
Augmented reality: Augmented reality (AR) is a technology that overlays digital information, such as images, sounds, or data, onto the real world, enhancing the user's perception of their environment. This technology allows for immersive storytelling by combining digital elements with physical surroundings, creating a more engaging experience for users. AR can also be used to visualize complex data and information in a way that is accessible and interactive.
Bar Charts: Bar charts are visual representations of data that use rectangular bars to show the quantity of different categories. Each bar's length or height corresponds to the value it represents, making it easy to compare data across various groups. Bar charts are commonly used in data journalism and analysis to provide clear insights and highlight trends in information.
Central tendency: Central tendency refers to a statistical measure that identifies a single value as representative of an entire dataset, providing insight into the average or typical value within that data. It includes key metrics like the mean, median, and mode, which help summarize large sets of data and make them easier to interpret. Understanding central tendency is crucial in analyzing trends and making comparisons in various contexts, including social phenomena and economic data.
Chi-square tests: Chi-square tests are statistical methods used to determine if there is a significant association between categorical variables. By comparing the observed frequencies of events with the expected frequencies under the assumption of no association, these tests help journalists and analysts uncover patterns in data, especially in data journalism and analysis.
Computational journalist: A computational journalist is a professional who integrates data analysis, coding, and traditional journalism to uncover and present stories using quantitative data. They harness technology and algorithms to analyze large datasets, visualize information, and generate insights that can lead to impactful storytelling. This role has become increasingly important as the volume of data available grows, allowing journalists to engage audiences with data-driven narratives.
Confidence intervals: A confidence interval is a range of values used to estimate the true value of a population parameter, indicating the degree of uncertainty associated with a sample statistic. This statistical tool is crucial in data analysis, as it provides a way to quantify the reliability of estimates, giving journalists insight into the precision and variability of their findings.
Confounding Variables: Confounding variables are extraneous factors that can influence both the independent and dependent variables in a study, potentially skewing the results and making it difficult to determine the true relationship between them. In data journalism and analysis, identifying and controlling for confounding variables is crucial for drawing accurate conclusions from data. Failure to account for these variables can lead to misleading interpretations and undermine the credibility of the findings.
Correlation vs causation: Correlation refers to a statistical relationship between two variables, indicating how they move together, while causation implies that one variable directly affects or causes changes in another. Understanding the difference is crucial in data analysis and journalism to avoid misleading interpretations and assertions. Identifying whether a correlation is merely coincidental or indicative of a causal relationship can significantly impact reporting and the audience's understanding of the data presented.
Crowdsourcing: Crowdsourcing is the practice of obtaining information, ideas, or services from a large group of people, often through online platforms. It allows journalists to tap into the collective knowledge and experience of the public to enhance their storytelling, gather data, and conduct research. By leveraging the power of the crowd, journalists can gain diverse perspectives and resources that may not be available through traditional methods.
CSV: CSV stands for Comma-Separated Values, a simple file format used to store tabular data such as spreadsheets or databases. Each line in a CSV file corresponds to a row in the table, and each value is separated by a comma, making it easy to read and write data using various programming languages or software applications. This format is widely used in data journalism and analysis because it allows for straightforward data manipulation and sharing across different platforms.
D3.js: d3.js is a JavaScript library used for producing dynamic, interactive data visualizations in web browsers. It leverages web standards like SVG, HTML5, and CSS to bring data to life, allowing journalists and data analysts to create engaging visual representations of complex datasets.
Data acquisition: Data acquisition refers to the process of collecting and measuring information from various sources for analysis and reporting. It serves as the foundational step in data journalism, where journalists gather data to uncover trends, inform stories, and provide evidence-based insights. This process involves not only the collection of quantitative data but also qualitative information that can enrich narratives and help convey complex issues more effectively.
Data analysis: Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It plays a critical role in extracting meaningful insights from large datasets, particularly in journalism, where it enhances storytelling and provides evidence for claims. By leveraging data analysis techniques, journalists can uncover trends, patterns, and correlations that may not be immediately visible, ultimately enriching their reporting and enabling more informed public discourse.
Data cleaning: Data cleaning is the process of detecting and correcting or removing inaccurate, incomplete, or irrelevant data from a dataset. This step is crucial in data journalism and analysis, as it ensures the integrity and accuracy of the information used for reporting and decision-making. Clean data helps journalists to derive meaningful insights and present reliable narratives based on factual evidence.
Data dictionaries: Data dictionaries are centralized repositories that contain metadata, or data about data, describing the structure, relationships, and usage of data elements within a database or system. They play a crucial role in data management by providing definitions, formats, and constraints for each data field, which helps ensure consistency and understanding among users when working with data sets.
Data journalism: Data journalism is a reporting approach that relies on the analysis and visualization of quantitative data to uncover insights, tell stories, and enhance the accountability of institutions. It transforms raw data into engaging narratives that can inform the public, shedding light on important issues while complementing traditional journalistic practices.
Data journalist: A data journalist is a professional who utilizes data analysis and visualization to uncover stories, trends, and insights in news reporting. This role combines traditional journalism skills with statistical knowledge and technical expertise to present complex information in an understandable way. By leveraging various data sources, data journalists help enhance storytelling, make information accessible, and support accountability in reporting.
Data literacy: Data literacy is the ability to read, understand, create, and communicate data as information. This skill set enables individuals to interpret and utilize data effectively in decision-making processes, which is crucial for data journalism and analysis. As data becomes an essential part of storytelling in journalism, being data literate means being able to critically assess and convey data-driven insights to an audience.
Data privacy: Data privacy refers to the proper handling, processing, and storage of personal information to protect individuals' rights and maintain their confidentiality. It encompasses the policies and practices that organizations adopt to safeguard sensitive data, ensuring that it is only accessible to authorized users and used appropriately. In today's digital landscape, data privacy is crucial as it relates to trust, security, and compliance with regulations.
Data quality: Data quality refers to the overall utility of a dataset, determined by its accuracy, completeness, reliability, and relevance for its intended purpose. High data quality ensures that the information derived from datasets is trustworthy and useful for analysis, which is crucial in data journalism where facts and figures are used to tell stories and inform the public.
Data validation: Data validation is the process of ensuring that data is accurate, complete, and meets specific criteria before it is used for analysis or reporting. This process involves checking the quality and reliability of data sources to prevent misinformation, making it a crucial step in maintaining integrity in journalism and data-driven storytelling. By validating data, journalists can confirm its authenticity and relevance, ensuring that their work is built on a solid foundation of reliable information.
Data visualization: Data visualization is the graphical representation of information and data, utilizing visual elements like charts, graphs, and maps to communicate complex data clearly and effectively. This technique allows journalists to interpret large datasets and present them in an engaging way, making it easier for audiences to grasp key insights and trends. By transforming raw data into visual formats, data visualization enhances storytelling in journalism and supports the analysis of information.
Data-driven narratives: Data-driven narratives are storytelling methods that rely on data and analytics to inform, shape, and support the narrative presented in journalistic work. This approach combines quantitative data with qualitative insights to create compelling and informative stories that resonate with audiences. By using data as a foundation, journalists can uncover trends, patterns, and insights that traditional reporting might overlook, enhancing the overall credibility and impact of their stories.
Descriptive statistics: Descriptive statistics refers to a set of statistical techniques that summarize and organize data to provide a clear overview of its main features. This includes measures such as mean, median, mode, and standard deviation, which help to describe the central tendency and variability within a dataset. These statistics are essential in data journalism as they allow journalists to present complex information in an accessible way, making it easier for the audience to understand trends and patterns in the data.
Duplicates: In data journalism, duplicates refer to instances where identical records appear more than once within a dataset. This can lead to misleading analyses, inflated statistics, and erroneous conclusions if not properly managed. Identifying and handling duplicates is essential for ensuring the integrity and accuracy of data-driven stories.
Effect Sizes: Effect sizes are quantitative measures that help to determine the magnitude of a difference or relationship in data analysis. They provide context beyond p-values, helping to gauge how substantial a finding is, which is crucial for understanding the practical significance of research results in data journalism.
Effective data visualization: Effective data visualization is the practice of using graphical representations to present data in a clear and insightful manner, making complex information more accessible and understandable. This approach helps audiences quickly grasp key insights, trends, and patterns in the data, facilitating better decision-making and communication. Utilizing appropriate visuals not only enhances comprehension but also engages viewers, making the information memorable and impactful.
Excel: Excel is a powerful spreadsheet software developed by Microsoft that enables users to organize, analyze, and visualize data in a structured format. Its capabilities include calculations, graphing tools, pivot tables, and a macro programming language called VBA. By using Excel, journalists can effectively manage large sets of data, conduct in-depth analyses, and create compelling visualizations to support their stories.
FOIA Requests: FOIA requests refer to formal requests made under the Freedom of Information Act (FOIA) that allow individuals to obtain access to records and information held by federal agencies in the United States. This process is a crucial tool for transparency and accountability, enabling journalists, researchers, and the public to access government data that can lead to informed discussions and investigations.
Freedom of Information Act: The Freedom of Information Act (FOIA) is a law that allows individuals to request access to information held by the federal government. This act is crucial for promoting transparency and accountability in government, enabling journalists and the public to obtain documents and records that can shed light on government actions and decisions.
Geospatial data visualization: Geospatial data visualization is the graphical representation of data related to geographic locations, allowing for the analysis and interpretation of spatial patterns and relationships. This technique combines geographic information systems (GIS) with data visualization tools to present complex datasets in an accessible way, making it easier to understand trends, correlations, and outliers. Effective geospatial data visualization can enhance storytelling in data journalism by presenting information that connects a narrative to specific locations.
Ggplot2: ggplot2 is a data visualization package for the R programming language, designed to create complex graphics using a coherent and flexible framework. It uses a layered approach to building plots, allowing users to incrementally add components such as points, lines, and text to their visualizations. This package is especially popular in data journalism and analysis for its ability to produce high-quality, customizable graphics that effectively communicate data-driven stories.
Government databases: Government databases are organized collections of data maintained by government entities that provide public access to information for transparency, research, and data analysis. These databases can include a wide range of information, such as census data, economic statistics, crime reports, and public health records, playing a crucial role in data journalism and analysis.
Hypothesis tests: Hypothesis tests are statistical methods used to determine whether there is enough evidence in a sample of data to support a particular belief or hypothesis about a population parameter. These tests help journalists analyze data and draw conclusions by assessing the likelihood that observed results occurred by chance. They play a crucial role in data journalism, allowing for informed decision-making based on empirical evidence.
Inferential Statistics: Inferential statistics is a branch of statistics that allows researchers to make conclusions and predictions about a population based on a sample of data drawn from that population. This method involves analyzing the sample data to infer properties or trends about the larger group, often using techniques such as hypothesis testing and confidence intervals. It is essential for drawing meaningful insights from data journalism, enabling journalists to report on trends and make claims based on statistical evidence.
Infographics: Infographics are visual representations of information, data, or knowledge that aim to present complex information quickly and clearly. They often combine graphics, charts, and text to convey messages efficiently, making them ideal for enhancing storytelling and simplifying analysis in various forms of media.
Interactive visualizations: Interactive visualizations are dynamic graphical representations of data that allow users to engage with the information through manipulation and exploration. They empower users to adjust variables, filter datasets, and gain deeper insights by interacting directly with the visual elements. This level of engagement enhances the understanding of complex data stories and promotes a more personalized experience in data journalism.
Investigative reporting: Investigative reporting is a journalistic practice that involves in-depth and thorough research to uncover hidden information, often related to issues of public interest, wrongdoing, or corruption. This type of reporting goes beyond surface-level news, aiming to reveal the truth through extensive fact-checking, interviews, and data analysis, thus providing a more complete narrative about complex issues. It connects closely to the need for crafting balanced news stories, utilizing data effectively, producing detailed long-form narratives, and managing crises through informed communication.
Json: JSON, which stands for JavaScript Object Notation, is a lightweight data interchange format that is easy for humans to read and write, and easy for machines to parse and generate. It is widely used in web applications to transmit data between a server and a client, making it an essential tool for data journalism and analysis where structured data is necessary for storytelling.
Line Graphs: Line graphs are a type of chart used to display information that changes over time, showcasing trends by connecting individual data points with straight lines. They are particularly useful for illustrating continuous data and allow viewers to quickly grasp patterns, peaks, and troughs in the information presented. Line graphs effectively communicate relationships between two variables, making them a powerful tool in data journalism and analysis.
Margins of Error: Margins of error represent the range within which the true values of a population parameter are expected to lie, given a sample statistic. They are crucial in statistics as they indicate the level of uncertainty in survey results or data analysis, helping readers to understand the potential variability in reported figures.
Mean: The mean is a statistical measure that represents the average value of a set of numbers, calculated by summing all values and dividing by the number of values. In data journalism, the mean is crucial for summarizing and interpreting data trends, providing a simple way to understand complex datasets and facilitate comparisons across different groups or categories.
Median: The median is the middle value in a set of numbers when they are arranged in order. It serves as a measure of central tendency, dividing a dataset into two equal halves, which can help to provide insights into the overall distribution of data points, especially in the context of outliers and skewed data.
Missing values: Missing values refer to the absence of data points in a dataset, which can occur for various reasons, such as errors in data collection or specific data not being applicable. In data journalism and analysis, understanding missing values is crucial because they can affect the accuracy of analyses and interpretations, potentially leading to misleading conclusions if not handled correctly. The treatment of missing values often requires careful consideration to maintain data integrity and provide meaningful insights.
Mode: In statistics, the mode refers to the value that appears most frequently in a data set. It is a measure of central tendency, alongside the mean and median, and helps to identify the most common value within a collection of data. Understanding the mode is essential for interpreting data effectively, especially when analyzing trends or patterns in newsworthy events.
Multivariate analysis: Multivariate analysis is a statistical technique used to understand the relationship between multiple variables at the same time. This method helps to identify patterns, correlations, and trends among the variables, providing deeper insights into complex data sets. It's essential in data journalism as it enables journalists to make sense of intricate relationships within the data they analyze, leading to more informed storytelling.
Outliers: Outliers are data points that differ significantly from other observations in a dataset, often appearing as extreme values. They can indicate variability in measurement, experimental errors, or novel insights, making them crucial for accurate data analysis and interpretation.
P-hacking: P-hacking refers to the practice of manipulating data or statistical analyses to achieve a desired p-value, typically below the conventional threshold of 0.05, which indicates statistical significance. This can involve selectively reporting results, altering data sets, or conducting multiple analyses without proper disclosure, ultimately leading to misleading conclusions in research. Such practices undermine the integrity of scientific research and can distort the public understanding of data-driven findings.
Probability Theory: Probability theory is a branch of mathematics that deals with the likelihood of events occurring. It provides a framework for quantifying uncertainty, which is crucial in data journalism as it helps journalists make sense of data trends and inform their storytelling with statistical evidence.
Programming: In the context of data journalism and analysis, programming refers to the process of writing code to manipulate, analyze, and visualize data. This skill allows journalists to efficiently handle large datasets, automate repetitive tasks, and create interactive visualizations that enhance storytelling. By utilizing programming languages, journalists can uncover patterns and insights from data that might not be visible through traditional reporting methods.
Python: Python is a high-level programming language known for its readability and simplicity, making it a popular choice for various applications, including data analysis, web development, and automation. Its extensive libraries and frameworks support tasks in data journalism, enabling journalists to gather, analyze, and visualize data effectively.
R: In the context of investigative reporting and data journalism, 'r' typically refers to a programming language and software environment used for statistical computing and data analysis. It is essential for journalists to utilize 'r' for handling large datasets, performing complex analyses, and visualizing data, thus enhancing the depth and accuracy of their reporting.
Range: In data journalism, range refers to the difference between the highest and lowest values in a dataset. This concept helps journalists and analysts understand the spread of data points, allowing for insights into trends, variations, and outliers within the information they are analyzing.
Regression analysis: Regression analysis is a statistical method used to determine the relationship between variables, allowing for predictions and insights based on data. It helps journalists uncover trends and make sense of complex datasets by analyzing how one variable may affect another, which is crucial in data journalism and analysis.
Scatter plots: A scatter plot is a type of data visualization that uses dots to represent the values obtained for two different variables, plotted along two axes. This visual representation helps in identifying relationships, trends, and correlations between the variables, making it a powerful tool for analysis in various fields, including data journalism. Scatter plots are particularly useful for showing how one variable may change in relation to another, allowing for a clearer understanding of data patterns and distributions.
Selection bias: Selection bias occurs when the individuals included in a study or analysis are not representative of the larger population, leading to skewed or misleading results. This bias can arise from the way data is collected or the criteria used for selecting participants, often affecting the validity of conclusions drawn from the data.
Small multiples technique: The small multiples technique is a data visualization approach that presents multiple graphs or charts in a grid layout, allowing for easy comparison across different datasets or time periods. This method enhances the viewer's ability to recognize patterns, trends, and differences within the data, making it a powerful tool in data journalism and analysis. By displaying similar types of information side by side, the small multiples technique helps convey complex information in a more digestible format.
SQL: SQL, or Structured Query Language, is a standardized programming language used for managing and manipulating relational databases. It allows users to perform various operations such as querying data, updating records, and creating or modifying database structures. This language is vital for investigative reporting and data journalism as it enables reporters to access large datasets, extract meaningful information, and analyze trends that can support their stories.
Standard Deviation: Standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data points. It tells us how spread out the numbers in a data set are relative to the mean (average). A low standard deviation indicates that the data points tend to be close to the mean, while a high standard deviation indicates that the data points are spread out over a wider range of values, which is crucial for understanding trends and patterns in data journalism.
Statistical analysis: Statistical analysis is the process of collecting, reviewing, and interpreting data to discover patterns and trends that can inform decisions and conclusions. This technique is crucial in data journalism as it enables journalists to turn raw data into meaningful insights, helping to tell compelling stories backed by numbers.
T-tests: A t-test is a statistical method used to determine if there is a significant difference between the means of two groups. This technique is particularly useful in data analysis, especially when the sample sizes are small and the population standard deviation is unknown. T-tests help journalists and researchers draw conclusions from data sets, making it easier to interpret findings and present credible information in their work.
Tableau: A tableau is a visual representation of data that organizes and presents information in a structured format, making it easier to analyze trends and patterns. In data journalism, it allows for the effective communication of complex data sets by transforming them into clear, understandable visuals that can engage an audience. By using tableaus, journalists can highlight significant findings and convey stories through data.
Transparency: Transparency in journalism refers to the practice of being open and clear about the sources of information, methods used in reporting, and potential biases that may affect the content. This principle helps build trust with audiences by ensuring that they understand how news is gathered and presented, fostering a more informed public.
User Experience Design: User experience design (UX design) is the process of enhancing user satisfaction by improving the usability, accessibility, and pleasure provided in the interaction between the user and the product. This approach focuses on understanding user needs, behaviors, and the context of use, making it critical for creating effective data journalism tools that resonate with audiences.
Variability: Variability refers to the degree to which data points in a set differ from each other and from the mean of the dataset. It is a crucial concept in data journalism and analysis, as it helps to understand the spread and distribution of data, which can reveal trends, anomalies, and patterns that are essential for storytelling and reporting.
Virtual reality: Virtual reality (VR) is a simulated experience that can be similar to or completely different from the real world, achieved through the use of technology such as headsets, sensors, and computers. This immersive experience allows users to interact with a 3D environment and enhances storytelling by providing engaging and interactive narratives. VR can transform how information is presented and consumed, bridging the gap between traditional media and emerging digital formats.
Visualization specialist: A visualization specialist is a professional who focuses on creating visual representations of data to make complex information more understandable and accessible. They use various tools and techniques to transform raw data into graphics, charts, and interactive displays that help users grasp insights quickly. This role is essential in data journalism and analysis, as it allows for the effective communication of data-driven stories.
Web scraping: Web scraping is the automated process of extracting large amounts of data from websites, allowing users to gather information quickly and efficiently. This technique is essential in various fields, enabling data journalists, researchers, and businesses to analyze trends, gather insights, and inform decision-making. By using web scraping tools and techniques, individuals can collect data from public web pages without the need for manual entry, making it a powerful resource for data journalism and research.
XML: XML, or Extensible Markup Language, is a flexible text format designed to store and transport data. It allows for the creation of custom tags that describe the data's structure and meaning, making it easier to share information across different systems and platforms. In data journalism, XML plays a critical role by enabling the representation of complex datasets in a structured way, which can be parsed and analyzed by various software tools.