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Data Journalism ( Powerpoint Presentation)

Data Journalism ( Powerpoint Presentation)

Original price was: $5.00.Current price is: $3.00.

In an era where data is everywhere, the ability to transform raw numbers into compelling stories is a crucial skill for modern journalists. This comprehensive course, “Data Journalism: Telling Stories with Data,” is designed to equip aspiring and professional journalists with the tools and techniques to incorporate data into their storytelling. The course covers the entire data journalism process—from sourcing and analyzing data, to visualizing and presenting it for different media platforms.

Participants will learn how to find, clean, and evaluate datasets from various sources while adhering to ethical and legal standards. With hands-on sessions, students will develop proficiency in using data tools such as spreadsheets, visualization software, and programming languages like Python or R. They will explore data-driven investigations, write data-informed stories, and create interactive graphics, all while learning to communicate their findings to both technical and non-technical audiences.

The course also addresses the future of data journalism, examining emerging trends like AI, machine learning, and the growing importance of data in investigative reporting. By the end of the course, students will have developed a full data-driven project and gained the confidence to tackle complex data stories, whether for print, broadcast, or digital platforms.

Key Topics Include:

  • Data sourcing, cleaning, and verification
  • Data analysis techniques and tools
  • Visualization best practices for compelling storytelling
  • Investigative data journalism methods
  • Legal and ethical issues in data journalism
  • Future trends in data journalism (AI, machine learning)
  • Developing long-form, data-driven investigations

This course is perfect for journalists, editors, and communications professionals eager to harness the power of data to enhance their reporting and storytelling. No prior data experience is required, but a basic understanding of journalism principles is recommended.

Category:

Week 1: Introduction to Data Journalism

  • 1.1 Definition and Scope of Data Journalism
    • What is data journalism?
    • How data enhances traditional journalism.
  • 1.2 The Role of Data in Modern Journalism
    • Case studies: Successful data-driven journalism projects.
    • The growing demand for data literacy in newsrooms.
  • 1.3 Tools and Technologies for Data Journalism
    • Overview of tools: Spreadsheets, data scraping tools, visualization software.

Week 2: Data Sourcing and Collection

  • 2.1 Finding Reliable Data Sources
    • Public datasets (government, NGOs, etc.).
    • Web scraping and API usage.
  • 2.2 Evaluating Data for Accuracy and Credibility
    • How to verify data sources.
    • Identifying biases in datasets.
  • 2.3 Data Ethics
    • Privacy concerns, informed consent, and responsible use of data.

Week 3: Data Analysis and Interpretation

  • 3.1 Basics of Data Analysis
    • Introduction to spreadsheets (Excel, Google Sheets).
    • Data cleaning, sorting, and filtering.
  • 3.2 Identifying Trends and Patterns
    • Using pivot tables, statistical functions, and formulas.
    • Basic data analysis with R or Python for journalism.
  • 3.3 Storytelling with Data
    • Turning raw data into compelling narratives.

Week 4: Data Visualization Techniques

  • 4.1 Introduction to Data Visualization
    • Why visualizing data matters.
    • Types of charts and graphs and when to use them.
  • 4.2 Tools for Data Visualization
    • Hands-on with tools: Datawrapper, Tableau, Flourish.
    • Visualizing complex datasets (mapping tools, interactive graphics).
  • 4.3 Best Practices in Data Visualization
    • Ensuring clarity and accuracy in charts.
    • Avoiding common visualization mistakes.

Week 5: Investigative Data Journalism

  • 5.1 Introduction to Investigative Journalism Using Data
    • Examples of data-driven investigations (Panama Papers, COVID-19 reporting).
  • 5.2 Advanced Data Collection Techniques
    • Web scraping with Python.
    • Using FOIA requests to obtain public records.
  • 5.3 Data Mining and Big Data
    • Understanding how to work with large datasets.
    • Using big data for investigative journalism.

Week 6: Fact-Checking and Data Verification

  • 6.1 Verifying Data and Sources
    • Techniques to fact-check data sources.
    • Understanding errors and outliers.
  • 6.2 Cross-Referencing Multiple Data Sources
    • Combining different datasets for a complete picture.
    • Collaborating with experts in fields like economics, science, and public policy.

Week 7: Data Journalism for Different Mediums

  • 7.1 Data Journalism for Print and Digital
    • Writing stories based on data for newspapers and online outlets.
  • 7.2 Broadcasting Data-Driven Stories
    • Presenting data on television and radio.
    • Incorporating data in video journalism and social media.
  • 7.3 Interactive and Immersive Data Journalism
    • Building interactive web stories using data.
    • Examples of successful interactive journalism projects.

Week 8: Reporting on Specific Topics Using Data

  • 8.1 Data Journalism in Health and Science
    • Reporting on health data (e.g., pandemic, public health statistics).
    • Visualizing scientific data and research.
  • 8.2 Financial and Economic Data Journalism
    • Reporting on business, stock markets, and economic trends using data.
  • 8.3 Political and Social Data Reporting
    • Elections, policy changes, social justice issues: Analyzing and reporting.

Week 9: Data-Driven Investigations and Long-Form Reporting

  • 9.1 Developing a Data-Driven Investigation
    • Planning and executing a long-term data investigation.
  • 9.2 Collaborating on Large-Scale Data Projects
    • How journalists can collaborate across borders on global investigations.
  • 9.3 Presenting Data-Driven Long-Form Stories
    • Structuring long-form content that integrates data analysis seamlessly.

Week 10: Tools for Collaboration and Communication

  • 10.1 Using Version Control in Data Projects
    • Introduction to Git and GitHub for collaboration.
  • 10.2 Effective Collaboration in Data Journalism
    • Managing projects with multiple journalists and data scientists.
  • 10.3 Communicating Findings to Non-Technical Audiences
    • Translating complex data into accessible stories.

Week 11: Legal and Ethical Issues in Data Journalism

  • 11.1 Legal Considerations
    • Copyright and licensing of data.
    • Using proprietary vs. open-source data.
  • 11.2 Ethical Reporting with Data
    • Avoiding manipulation of data in journalism.
    • Addressing privacy issues in reporting.

Week 12: Future Trends in Data Journalism

  • 12.1 Emerging Tools and Technologies
    • AI, machine learning, and automation in journalism.
  • 12.2 Challenges in the Future of Data Journalism
    • Data accessibility, misinformation, and disinformation.
  • 12.3 The Role of Data Journalism in Shaping Public Policy
    • How data journalism can influence policy decisions and public opinion.

Week 13: Practical Project: Developing a Data-Driven Story

  • 13.1 Project Planning
    • Choosing a dataset and identifying a story.
    • Developing a project plan for a data-driven report.
  • 13.2 Project Execution
    • Gathering, cleaning, and analyzing data.
    • Writing and visualizing the final report.
  • 13.3 Peer Review and Feedback
    • Presenting the project to classmates and getting feedback.

Week 14: Final Presentation and Evaluation

  • 14.1 Presenting Final Data Journalism Projects
    • Presenting the findings and visualizations.
  • 14.2 Peer and Instructor Feedback
    • Evaluation of storytelling, data accuracy, and visualization.
  • 14.3 Course Wrap-Up and Future Opportunities
    • How to continue developing data journalism skills after the course.

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