Course outline
Module 1
Module 1: Introduction to Data Journalism
Introduction to Data Journalism
Knowledge check
Q1. What is the primary goal of data journalism?
- A. To create visually appealing charts
- B. To uncover and tell stories using data ✓
- C. To collect large amounts of data
- D. To publish news articles without using data
Why: Data journalism focuses on using data to uncover and tell compelling stories that inform the public.
Q2. Which of the following is a common source of data for journalists?
- A. Only government databases
- B. Social media, surveys, and public records ✓
- C. Only private company data
- D. Only academic research papers
Why: Journalists often use a variety of sources, including social media, surveys, and public records, to gather data for their stories.
Q3. What is one key skill required for data journalism?
- A. Writing poetry
- B. Data analysis and visualization ✓
- C. Playing musical instruments
- D. Cooking
Why: Data analysis and visualization are essential skills for effectively interpreting and presenting data in journalism.
Q4. Which of the following best describes the role of a data journalist?
- A. To write fiction stories
- B. To interpret data and present it in a way that informs the public ✓
- C. To only collect data without analyzing it
- D. To focus solely on photography
Why: A data journalist interprets data and presents it in an informative way that helps the public understand complex issues.
Q5. What is a common tool used in data journalism for data visualization?
- A. Microsoft Word
- B. Adobe Photoshop
- C. Tableau or Google Data Studio ✓
- D. Excel only
Why: Tools like Tableau and Google Data Studio are commonly used for creating visualizations in data journalism.
Module 2
Module 2: Finding and Evaluating Data Sources
Finding and Evaluating Data Sources
Knowledge check
Q1. Which of the following is a primary source of data?
- A. A textbook
- B. A government census report ✓
- C. A news article
- D. A research paper summary
Why: Government census reports are direct sources of data collected by official organizations.
Q2. What is the first step in evaluating a data source?
- A. Check the author's credentials
- B. Verify the source's credibility and reliability ✓
- C. Look for the date of publication
- D. Check for peer reviews
Why: Verifying the credibility and reliability of the source is essential before considering other details.
Q3. Which of the following is a secondary source of data?
- A. A survey conducted by a university
- B. A book that analyzes survey results ✓
- C. A raw dataset from a government agency
- D. A scientific experiment log
Why: A book that analyzes survey results is a secondary source as it interprets primary data.
Q4. What should you look for when checking the timeliness of data?
- A. The number of citations
- B. The source's website design
- C. The date of publication or data collection ✓
- D. The author's social media presence
Why: The date of publication or data collection helps determine how current the data is.
Q5. Which of the following is a sign of a reliable data source?
- A. Lack of citations
- B. Unverifiable claims
- C. Clear methodology and transparency ✓
- D. Biased language
Why: Clear methodology and transparency are indicators of a reliable and trustworthy data source.
Module 3
Module 3: Data Cleaning and Preparation
Data Cleaning and Preparation
Knowledge check
Q1. What is the primary goal of data cleaning?
- A. To collect more data
- B. To ensure data accuracy and consistency ✓
- C. To create visualizations
- D. To increase data complexity
Why: Data cleaning focuses on improving data quality by identifying and correcting errors, inconsistencies, and inaccuracies.
Q2. Which of the following is a common data cleaning task?
- A. Designing a database schema
- B. Removing duplicate records ✓
- C. Writing a news article
- D. Conducting interviews
Why: Removing duplicate records is a common data cleaning task that helps maintain data integrity.
Q3. What does data normalization typically involve?
- A. Adding more data sources
- B. Changing the format of data to a standard ✓
- C. Creating charts and graphs
- D. Deleting irrelevant data
Why: Data normalization involves standardizing data formats to ensure consistency across datasets.
Q4. Which tool is commonly used for data cleaning in data journalism?
- A. Photoshop
- B. Excel ✓
- C. Canva
- D. Word
Why: Excel is a widely used tool for data cleaning due to its powerful data manipulation and analysis features.
Q5. Why is it important to handle missing data during the cleaning process?
- A. To make the dataset look more complete
- B. To avoid biased or incorrect analysis results ✓
- C. To increase the number of data points
- D. To make the data more visually appealing
Why: Missing data can lead to biased or incorrect analysis, making it crucial to address during the cleaning process.
Module 4
Module 4: Visualizing Data with Tools
Visualizing Data with Tools
Knowledge check
Q1. Which of the following tools is commonly used for creating interactive data visualizations on the web?
- A. Microsoft Excel
- B. Tableau ✓
- C. Google Docs
- D. PowerPoint
Why: Tableau is widely recognized for its capabilities in creating interactive and dynamic data visualizations suitable for web use.
Q2. What is the primary purpose of data visualization in journalism?
- A. To make data look more attractive
- B. To simplify complex data for better understanding ✓
- C. To replace text with images
- D. To increase the length of articles
Why: The main purpose of data visualization in journalism is to simplify complex data, making it easier for the audience to understand and interpret.
Q3. Which file format is commonly used for exporting data visualizations for use in web pages?
- A. .docx
- B. .csv
- C. .png
- D. .html ✓
Why: .html files are commonly used for embedding interactive visualizations directly into web pages.
Q4. Which of the following is a free data visualization tool that can be used in a web browser?
- A. Adobe Illustrator
- B. D3.js ✓
- C. Photoshop
- D. Inkscape
Why: D3.js is a free, open-source JavaScript library that allows for the creation of dynamic and interactive data visualizations in web browsers.
Q5. What is a common benefit of using charts and graphs in data journalism?
- A. They increase the complexity of the data
- B. They make the data more engaging and easier to understand ✓
- C. They reduce the need for data analysis
- D. They replace the need for written reports
Why: Charts and graphs make data more engaging and easier for the audience to understand, enhancing the storytelling aspect of data journalism.
Module 5
Module 5: Writing with Data: Storytelling Techniques
Writing with Data: Storytelling Techniques
Knowledge check
Q1. What is the primary goal of storytelling with data in journalism?
- A. To present data in the most complex way possible
- B. To communicate information clearly and engage the audience ✓
- C. To hide the source of the data
- D. To use as many charts as possible
Why: The primary goal is to communicate information clearly and engage the audience, making the data accessible and meaningful.
Q2. Which of the following is a key element of effective data storytelling?
- A. Ignoring the audience's perspective
- B. Using clear and simple language ✓
- C. Overloading the story with unnecessary data
- D. Avoiding any form of visual representation
Why: Using clear and simple language helps ensure that the audience can easily understand the data being presented.
Q3. What should a journalist avoid when creating a data story?
- A. Using accurate data sources
- B. Including context that helps explain the data
- C. Making the story too complicated for the audience ✓
- D. Verifying the data before publishing
Why: Making the story too complicated can alienate the audience and reduce the effectiveness of the data storytelling.
Q4. Which of the following best describes the role of visuals in data storytelling?
- A. They are optional and can be skipped if time is limited
- B. They should always be used, regardless of the context
- C. They help to simplify complex data and enhance understanding ✓
- D. They are only useful for decorative purposes
Why: Visuals help to simplify complex data and enhance understanding, making the story more engaging and accessible.
Q5. What is an important consideration when selecting data for a story?
- A. Choosing data that supports a predetermined conclusion
- B. Selecting data that is relevant and meaningful to the story ✓
- C. Using data that is not publicly available
- D. Ignoring data that contradicts the main point
Why: Selecting data that is relevant and meaningful ensures that the story is accurate, informative, and impactful.
Module 6
Module 6: Ethics in Data Journalism
Ethics in Data Journalism
Knowledge check
Q1. What is the primary ethical consideration when handling data in journalism?
- A. Accuracy of the data source
- B. Privacy and consent of individuals ✓
- C. Speed of reporting
- D. Number of sources used
Why: Protecting the privacy and ensuring consent of individuals is a fundamental ethical responsibility in data journalism.
Q2. Which of the following is NOT a common ethical issue in data journalism?
- A. Misrepresentation of data
- B. Use of open-source data ✓
- C. Bias in data selection
- D. Fabrication of data
Why: Using open-source data is not inherently unethical, but how it is used and interpreted can raise ethical concerns.
Q3. Why is transparency important in data journalism?
- A. To increase the number of readers
- B. To allow readers to verify the data and analysis ✓
- C. To make the story more engaging
- D. To ensure faster publication
Why: Transparency allows readers to understand and verify the data and analysis, which is crucial for trust and credibility.
Q4. Which of the following is an example of ethical data journalism?
- A. Using unverified data from an anonymous source
- B. Correctly citing the source of the data used ✓
- C. Editing data to support a particular narrative
- D. Publishing data without context
Why: Properly citing the source of data is an ethical practice that supports accuracy and accountability.
Q5. What should a data journalist do if they discover an error in their published data story?
- A. Ignore it to avoid embarrassment
- B. Correct it immediately and inform the audience ✓
- C. Blame the data source
- D. Remove the story from publication
Why: Correcting errors promptly and informing the audience is an ethical responsibility that maintains trust and integrity.
Module 7
Module 7: Publishing and Presenting Data Stories
Publishing and Presenting Data Stories
Knowledge check
Q1. What is the primary purpose of data visualization in data journalism?
- A. To make the data look more attractive
- B. To communicate complex information clearly and efficiently ✓
- C. To increase the number of words in a story
- D. To replace text with images entirely
Why: Data visualization helps in conveying complex information in a clear and efficient manner, making it easier for the audience to understand the story behind the data.
Q2. Which of the following is a common tool used for creating data visualizations?
- A. Microsoft Word
- B. Google Sheets ✓
- C. Adobe Photoshop
- D. None of the above
Why: Google Sheets is a common tool used for creating basic data visualizations, although more advanced tools like Tableau or Python libraries are also used.
Q3. Which of the following is NOT a best practice for data storytelling?
- A. Using clear and concise language
- B. Including all data without filtering ✓
- C. Providing context for the data
- D. Using visual aids to support the narrative
Why: Including all data without filtering can overwhelm the audience and obscure the main message. It is important to filter and present relevant data.
Q4. What is the role of a data journalist when publishing a data story?
- A. To ignore the data and focus only on opinions
- B. To present the data in a way that is fair, accurate, and easy to understand ✓
- C. To manipulate the data to support a particular viewpoint
- D. To hide the sources of the data
Why: A data journalist's role is to present data in a fair, accurate, and understandable manner, ensuring transparency and credibility.
Q5. Which of the following is a key consideration when choosing a data visualization format?
- A. The number of colors used
- B. The relevance of the visualization to the story being told ✓
- C. The font size of the text
- D. The size of the image
Why: The relevance of the visualization to the story is crucial in ensuring that the data effectively supports the narrative and communicates the intended message.