Data Journalism

Unit 1 – Data Journalism: Defining the Field

Unit 2 – Data Journalism: From Concept to Publication

Unit 3 – Finding Data: Strategies and Sources

Unit 4 – Data Cleaning: Ensuring Accuracy & Consistency

Unit 5 – Exploring Data: Finding Patterns and Trends

Unit 6 – Statistical Analysis: Basics for Journalists

Unit 7 – Data Visualization Principles and Best Practices

Unit 8 – Data Viz: Static and Interactive Graphics

Unit 9 – Intro to Coding for Data Journalism

Unit 10 – Data Journalism: Managing & Querying Databases

Unit 11 – Data-Driven Storytelling: Compelling Narratives

Unit 12 – Ethics in Data Journalism: Key Principles

Unit 13 – Case Studies: Real-World Data Journalism

Unit 14 – Collaborative Data Journalism: Team Projects

Unit 15 – Data Journalism: Future Trends and Tech

## What do you learn in Data Journalism

You'll learn how to find, analyze, and visualize data to tell compelling news stories. The course covers data collection techniques, statistical analysis, data cleaning, and creating interactive visualizations. You'll also explore ethical considerations in data reporting and learn to use tools like Excel, SQL, and data visualization software to uncover and present meaningful insights.

## Is Data Journalism hard?

Data Journalism can be challenging, especially if you're not a numbers person. The stats and coding parts might seem daunting at first, but most students get the hang of it with practice. The trickiest part is often learning to think critically about data and spotting interesting stories within the numbers. Once you get past the initial learning curve, it's actually pretty fun.

## Tips for taking Data Journalism in college

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram 🌶️
2. Practice, practice, practice with real datasets
3. Team up with classmates for group projects
4. Stay updated on current events and data-driven news stories
5. Get comfortable with Excel and basic coding concepts early on
6. Don't be afraid to ask your prof for help with tricky statistical concepts
7. Watch "The Social Dilemma" for insights on data ethics and privacy issues
8. Read "The Art of Data Science" by Roger D. Peng for a deeper dive

## Common pre-requisites for Data Journalism

1. Introduction to Statistics: Covers basic statistical concepts, probability, and data analysis techniques. You'll learn how to interpret and present data using various statistical methods.

2. Intro to Computer Science: Provides a foundation in programming concepts and basic coding skills. This course will help you understand the logic behind data manipulation and analysis.

3. News Writing and Reporting: Focuses on the fundamentals of journalism, including writing styles, research methods, and ethical considerations in reporting. It's essential for understanding how to craft compelling stories around data.

## Classes similar to Data Journalism

1. Data Visualization: Explores techniques for creating effective visual representations of data. You'll learn to use tools like Tableau and D3.js to create interactive charts and infographics.

2. Investigative Journalism: Teaches advanced research and reporting techniques. This course often incorporates data analysis as a tool for uncovering and verifying information.

3. Digital Storytelling: Focuses on using multimedia elements to tell stories online. You'll learn how to integrate data visualizations with text, audio, and video content.

4. Computational Journalism: Combines programming skills with journalistic principles. This course dives deeper into using algorithms and machine learning for news gathering and analysis.

## Majors related to Data Journalism

1. Journalism: Focuses on gathering, verifying, and presenting news across various media platforms. Students learn reporting techniques, media ethics, and digital production skills.

2. Data Science: Combines statistics, computer science, and domain expertise to extract insights from data. Students learn advanced analytical techniques and programming skills for working with large datasets.

3. Information Science: Explores how information is created, stored, and used in various contexts. Students study data management, information systems, and human-computer interaction.

4. Digital Media: Focuses on creating and distributing content across digital platforms. Students learn about web design, social media strategy, and multimedia production.

## What can you do with a degree in Data Journalism?

1. Data Journalist: Work for news organizations to uncover and report stories hidden in data. You'll use your skills to analyze complex datasets and present findings in engaging, accessible ways.

2. Data Visualization Specialist: Create interactive graphics and visual stories for news outlets or other organizations. You'll translate complex data into easy-to-understand visuals that engage audiences.

3. Investigative Reporter: Use data analysis skills to uncover and verify information for in-depth news stories. You'll combine traditional reporting techniques with data-driven approaches to expose important issues.

4. Data Analyst for Non-Profits: Help organizations understand and communicate their impact through data. You'll analyze program outcomes and create reports to support fundraising and advocacy efforts.

## Data Journalism FAQs

1. Do I need to be good at math to succeed in Data Journalism? While some math skills are helpful, you don't need to be a math whiz. The focus is more on understanding and interpreting data rather than complex calculations.

2. What software will I learn in this course? You'll likely work with Excel, SQL databases, and data visualization tools like Tableau or D3.js. Some courses might also introduce basic programming in Python or R.

3. How is Data Journalism different from regular Journalism? Data Journalism relies more heavily on quantitative analysis to find and tell stories. It often involves working with large datasets and creating data visualizations to support reporting.

4. Can I specialize in a specific area of Data Journalism? Yes, many students focus on areas like environmental data, sports analytics, or political data journalism. These specializations often align with your interests and career goals.

Journalism

1.3 Role of data in modern journalism

1.4 Skills required for data journalists

1.2 Key concepts and terminology

1.1 Evolution and importance of data journalism

2.3 Data collection and analysis workflow

2.4 Publishing and presenting data stories

2.2 Planning and project management

2.1 Identifying data-driven story ideas

3.4 Creating original datasets through surveys and crowdsourcing

3.3 Freedom of Information requests

3.2 Web scraping and data extraction techniques

3.1 Open data sources and government databases

4.4 Documenting the cleaning process

4.3 Standardizing and formatting data

4.2 Data cleaning tools and techniques

4.1 Common data quality issues and their solutions

5.4 Time series analysis for temporal data

5.3 Correlation and relationship analysis

5.2 Data distribution and outlier detection

5.1 Descriptive statistics and summary measures

6.3 Regression analysis

6.2 Hypothesis testing and statistical significance

6.1 Probability and sampling

6.4 Statistical software for journalists

7.1 Visual perception and cognitive principles

7.2 Choosing appropriate chart types

7.3 Color theory and accessibility in data vis

7.4 Designing effective infographics

8.1 Tools for creating static visualizations

8.3 Web-based visualization libraries and frameworks

8.4 Responsive design for multi-platform publishing

8.2 Introduction to interactive data visualization

9.1 Python for data analysis and visualization

9.3 JavaScript for interactive web visualizations

9.2 R for statistical computing and graphics

10.1 Relational database concepts

10.3 Database design for journalistic projects

10.2 SQL basics for data retrieval and manipulation

10.4 Working with large datasets and database optimization

11.1 Structuring data-driven stories

11.4 Fact-checking and verifying data-driven claims

11.3 Writing techniques for explaining complex data

11.2 Integrating data into traditional reporting

12.2 Protecting sources and ensuring data privacy

12.1 Ethical considerations in data collection and use

12.3 Transparency in methodology and data sources

12.4 Addressing bias and ensuring fairness in data journalism

13.1 Analysis of award-winning data journalism projects

13.2 Investigative reporting using data

13.3 Data journalism in different beats (politics, sports, environment)

13.4 Lessons learned from data journalism failures

14.2 Collaboration tools and project management

14.1 Building interdisciplinary teams for data projects

14.4 Cross-border and networked data journalism

14.3 Working with domain experts and data scientists

15.2 Data journalism in the age of big data and IoT

15.1 Artificial intelligence and machine learning in journalism

15.3 Augmented and virtual reality for data storytelling

15.4 Ethical challenges in emerging data technologies

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AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

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