big data

Top 5 Data Journalism Myths Busted

data journalism myths busted

A good journalist is a master storyteller. Telling compelling stories with data has become a new challenge and a growing trend in journalism today. The Internet has unlocked the world of information and made a huge amount of data available. Data journalists dig through all that data and transform it into valuable content.

There is a continuous and growing demand for data journalists, but many traditional journalists still shy away from it. Data journalism is kind of shrouded in mystery due to many myths about who can be a data journalist and which skills are required. We’ll delve into the most common myths about data journalism and unfold reality to help you better understand it and be less intimidated.

Myth #1: Data journalists must have a knack for math

“I am in journalism because I don’t have a knack for math” is the first thing most journalists and journalism students will say about data journalism. However, just because data-driven journalism involves numbers, it is not all about numbers. And it  certainly is not required that you be a math wizard to create stories with data.

As a matter of fact, not all data-driven stories have to rely on numbers. They can actually be based on something a lot more palatable, like wine, for example. If you don’t believe it, check out this interactive timeline that shows us how wine colonized the world.  

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What Budding Data Analysts Need To Know About Working With Big Data Tools

Tools for Big Data analysis

In some cases, employers may use the term “Data Analyst” to refer to an entry-level data scientist. Thus, a data analyst may be expected to take on the role of a data scientist, but at a junior level. In essence, theirs is a more technical role. They make discoveries from huge datasets, bringing structure to formless data and making analysis possible. They can find, manipulate and join data sources with other sources and clean the resulting dataset.

The work of a data analyst isn’t bound by industry. Analyzing data demands pretty much the same process everywhere, regardless of the nature of the data being used. You ask a question, do your research, get the relevant data, and explore it. Then from there you come up with a hypothesis on that data, start modeling, validating and drawing conclusions from it. Lastly, you reach the reporting stage where you visualize, summarize, and interpret that data.

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How To Clean Up Large PDF Datasets

Analyzing Data For Investigative Reporting

For big data analysts, working with clean data is a must. The major hurdle, though, is actually cleaning that data. Right now, analysts are spending more than half of their time cleaning up unstructured datasets. And if you aren’t an advanced expert with cleaning datasets, just knowing some basic data cleaning tasks becomes even more crucial.

Datasets can represent a large variety of information. From government and healthcare data to demographic and financial numbers, datasets come from all different areas. They also come in all different forms, like the PDF format. Getting it into a form you can manipulate is your first goal– and your biggest challenge.

The PDF format isn’t easily editable. In addition, it may contain hundreds of pages, consist of tables that span the entire file, be scanned in from a hard copy document, be created from an Excel spreadsheet, or be protected against copying and pasting.

You need to be able to analyze that locked down data. But how do you get started?

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How To Visualize Data Instantly With Google Sheets Explore

Excel Spreadsheet Diagram

Working with big datasets is never an easy task. Tons of spreadsheet data divided into rows and columns can be frustrating and stressful so anything to boost our productivity is more than welcome. And this is where companies like Google step in.

From time to time, Google releases a new update, becoming smarter and more practical than before. This happened just recently, when Google released an update for its spreadsheet application, Sheets, making it possible to visualize spreadsheet data even more easily than before.

If you haven’t explored this updated feature yet, here’s a closer look at how and why you should, especially if you work with data.

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How To Create Excel Pivot Tables

Excel Spreadsheet DiagramWe all use Microsoft Excel for different reasons. It’s a great tool for any basic or complex calculations because it’s so easy to adjust and edit your data. Besides this, there are several important features why someone would be using Excel on a daily basis:

  • To visualize data — Excel charts are not perfect, but they serve a great purpose for visualizing information and numerical data.
  • For checklists — It seems unusual, but people do use Excel to take notes, create tables and lists since it’s flexible, powerful and reliable.
  • To deal with a large quantity of data — Pivot tables and data filtering is useful and can help when you wish to find any info on complex data sets. Also, Excel allows you to perform all sorts of tasks regarding quick data manipulation.

Excel is amazing, but it isn’t surprising that the data imported into Excel spreadsheets oftentimes comes from a PDF document. It isn’t rare to find bank statements, various legal documents, account reports, and other corporate and federal data locked in PDF, data that needs to be sifted through and analyzed in Pivot tables.

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