Lillian Pierson

A wide range of tools is available that are designed to help big businesses and small take advantage of the data science revolution. Among the most essential of these tools are Microsoft Power BI, Tableau, SQL, and the R and Python programming languages.Comparing Microsoft Power BI and ExcelMicrosoft markets Power BI as a way to connect and visualize data using a unified, scalable platform that offers self-service and enterprise business intelligence that can help you gain deep insights into data.

By thinking through the how of a story, you are putting yourself in position to craft better data-driven stories. Looking at your data objectively and considering factors like how it was created helps you to discover interesting insights that you can include in your story. Also, knowing how to quickly find stories in potential data sources helps you to quickly sift through the staggering array of options.

A data-journalism piece is only as good as the data that supports it. To publish a compelling story, you must find compelling data on which to build. That isn't always easy, but it's easier if you know how to use scraping and autofeeds to your advantage. Scraping data Web-scraping involves setting up automated programs to scour and extract the exact and custom datasets that you need straight from the Internet so you don't have to do it yourself.

The human capacity to question and understand why things are the way they are is a clear delineation point between the human species and other highly cognitive mammals. Answers to questions about why help you to make better-informed decisions. These answers help you to better structure the world around you and help you develop reasoning beyond what you need for mere survival.

Data and stories are always more relevant to some places than others. From where is a story derived, and where is it going? If you keep these important facts in mind, the publications you develop are more relevant to their intended audience. The where aspect in data journalism is a bit ambiguous because it can refer to a geographical location or a digital location, or both.

As the old adage goes, timing is everything. It's a valuable skill to know how to refurbish old data so that it's interesting to a modern readership. Likewise, in data journalism, it's imperative to keep an eye on contextual relevancy and know when is the optimal time to craft and publish a particular story. When as the context to your story If you want to craft a data journalism piece that really garners a lot of respect and attention from your target audience, consider when — over what time period — your data is relevant.