Data journalism is a rapidly growing field that combines traditional journalism with data analysis and programming skills to uncover and tell impactful stories. According to a 2021 survey by the Google News Initiative, 52% of newsrooms now have a dedicated data journalist on staff, up from just 27% in 2017.
If you‘re curious about this exciting career at the intersection of reporting and data, read on. In this in-depth guide, we‘ll cover everything you need to know to become a data journalist, including:
- Key skills and tools to learn
- Education and training options
- Building a standout portfolio
- Finding data journalist jobs
- Insights and advice from data journalism pros
What is Data Journalism?
At its core, data journalism is the practice of finding stories in numbers and using data to inform reporting. Data journalists analyze datasets, look for meaningful trends and outliers, and use their findings to enhance traditional reporting and storytelling.
Some examples of what data journalists do:
- Scrape data from websites to build new datasets
- File Freedom of Information requests to obtain government data
- Clean and analyze data in spreadsheets or databases
- Look for newsworthy insights and leads in data
- Visualize data through charts, graphs and interactives
- Combine data analysis with interviews and on-the-ground reporting
The Growing Demand for Data Journalism Skills
As the amount of digital data grows, so does the need for journalists who can make sense of it all. According to Burning Glass, the number of job postings for data journalists has grown 30% in the past five years, compared to just 8% growth in journalism jobs overall.
Newsrooms are investing more in data-driven reporting because it allows them to:
- Find unique stories that competitors miss
- Provide concrete evidence to support investigative work
- Create engaging, shareable data visualizations
- Do impactful accountability journalism with lasting impact
With the public increasingly hungry for data-backed reporting, demand for data journalists‘ skills is unlikely to slow down anytime soon. The U.S. Bureau of Labor Statistics projects that the market for data science skills will grow by another 28% by 2026.
Key Skills for Data Journalists
To be competitive in the field, data journalists need to be comfortable working with numbers and learning new technologies in addition to traditional reporting skills. While you don‘t need to be an expert programmer, having some coding skills will make you a much more effective and efficient data journalist.
Some key technical skills to pick up:
1. Web Scraping
Much of the data you‘ll work with as a journalist isn‘t available in neat .CSV files – it‘s trapped in websites. That‘s where web scraping comes in. Web scraping is the process of programmatically extracting data from web pages.
Some popular tools and libraries for web scraping:
- BeautifulSoup (Python)
- Scrapy (Python)
- rvest (R)
- ParseHub (no code web scraping tool)
When scraping websites for data, it‘s important to be mindful of the site‘s terms of service and robots.txt, which specify if and how you‘re allowed to automatically access the site. Some sites will block excessive scraping requests. In those cases, you may need to slow down your scraper‘s rate limit, or distribute your requests across multiple IP addresses using proxies.
IP proxies allow you to route your scraping requests through intermediary IP addresses, which can help prevent your scraper from being blocked. Some reliable paid proxy services popular with data journalists include:
2. Data Cleaning and Analysis
Raw data is often messy and hard to work with. Data cleaning is the process of tidying up a dataset to make it consistent and error-free. This often involves:
- Reformatting dates and numbers
- Correcting inconsistent spelling and capitalization
- Handling missing values
- Merging data from multiple sources
- Filtering and subsetting data
Once your data is clean, you can start analyzing it to look for interesting trends, outliers and relationships. Some key data analysis skills:
- Basic statistics (mean, median, percentiles, etc.)
- Pivot tables and aggregation
- Merging and joining datasets
- Filtering and subsetting
- Regular expressions (regex)
Popular tools for data cleaning and analysis:
- Microsoft Excel
- Google Sheets
- Python (pandas, numpy)
- R (dplyr, tidyr)
- OpenRefine
3. Data Visualization
Visuals are a key part of data journalism. Well-designed data visualizations can make complex topics more accessible and engaging for readers. Some common types of visualizations used in data journalism:
- Bar charts
- Line charts
- Scatterplots
- Maps
- Network diagrams
When creating visualizations, always keep your audience in mind. What‘s the key takeaway you want them to get from the visual? Avoid overloading charts with too much information. Keep it simple and easy to interpret.
Some popular tools for data visualization:
- Datawrapper
- Tableau Public
- Flourish
- D3.js
- R (ggplot2)
Learning Data Journalism
You don‘t necessarily need a formal degree to become a data journalist, but getting some training will help you build your skills and make you a more competitive job applicant. There are a variety of ways to learn, ranging from free online tutorials to full degree programs.
Degree Programs
If you‘re starting from scratch, you might consider pursuing a degree that will give you a foundation in both journalism and data skills. Some relevant programs:
- Masters in Data Journalism (Columbia University)
- Masters in Computational Journalism (Stanford University)
- MS in Journalism – Data Journalism track (Columbia University)
- MS in Computer Science – Computation and Journalism track (Georgia Tech)
Online Courses
There are also many online courses that can help you pick up specific data journalism skills on a shorter timeline and at lower cost than a full degree. Some options:
- Data Journalism (EdX/Google News Initiative)
- Python for Data Journalism (DataCamp)
- Data Journalism and Visualization with R (Knight Center)
- SQL for Data Analysis (Udacity)
- Data Cleaning and Wrangling in R (Dataquest)
Bootcamps
For a more immersive educational experience, look into bootcamps that provide intensive, short-term training in data journalism skills. Some well-regarded programs:
- Lede Program for Data Journalism (Columbia University)
- Storytelling with Data Workshop (UC Berkeley Advanced Media Institute)
Self-Study
There are also plenty of free resources out there if you‘re motivated to teach yourself. Some great places to start:
- Data Journalism Handbook
- LearnDataJournalism.com Tutorials
- Data Journalism Blog
- FiveThirtyEight‘s Github repositories
Building a Data Journalism Portfolio
As with any journalism job, a strong portfolio is key to landing data journalist positions. Hiring managers will want to see examples of your past data journalism work.
If you don‘t have professional experience yet, consider:
- Doing data journalism projects on your own blog or website
- Contributing to open source data journalism projects on Github
- Collaborating with student media organizations or local news outlets on data stories
- Creating data visualizations and write-ups of public datasets
Make sure to show your process, not just polished final products. Hiring managers want to see how you approach obtaining, cleaning and analyzing data to find stories. Include links to Jupyter notebooks and Github repositories alongside your published stories.
Career Paths in Data Journalism
Many data journalists start out as data-savvy general assignment reporters or researcher/analysts on investigative teams. With more experience, you may move into a specialized data journalist role, or take on editorial leadership for a team of data reporters.
Job titles to look for:
- Data Reporter
- Data Journalist
- Investigative Data Journalist
- Data Visualization Journalist
- Computational Journalist
- Data Editor
Where data journalists work:
- National newspapers (New York Times, Washington Post)
- Broadcast news outlets (ABC News, NBC News)
- Digital news outlets (FiveThirtyEight, Vox)
- Nonprofit investigative newsrooms (ProPublica, Center for Public Integrity)
- Specialist journalism outlets (Politico, Kaiser Health News)
- University research centers (Stanford Computational Journalism Lab)
Advice from Data Journalism Pros
Finally, don‘t just take my word for it – hear from some leading data journalists on how they got into the field and their advice for aspiring data journalists:
"My biggest piece of advice for people looking to get into data journalism is to just start doing it. Find a dataset you‘re interested in and start digging. See if you can find a story in the numbers. And then write about your process and share your findings." –Maggie Lee, computational journalist at The Atlanta Journal-Constitution
"Develop a couple areas of expertise that you can really own – both in terms of beat knowledge and technical skills. Know more about education data than anyone in your newsroom. Or be the person who‘s really good at environmental data analysis and visualization." –Roberto Rocha, data journalist at CBC News
"Stay curious, and always be open to learning new things. Data journalism is changing so fast. The tools and techniques I used as a beginner just a few years ago already feel outdated. Never stop teaching yourself new skills, whether it‘s a new programming language like R or a new framework like d3.js." –Dana Amihere, data editor at KPPC
With the right skills, training, and a willingness to keep learning, data journalism can be a challenging but incredibly rewarding career. You‘ll play an important role in holding power to account, explaining complex issues, and telling compelling, high-impact stories backed by numbers. Go forth and start digging into data!