Data Journalism Course Modules-BAKU
  • Training Module for Data Journalism Course
  • DATA JOURNALISM IN THE NEWSROOM
  • 1. Module introduction
  • 2.The History of Data Journalism
  • 3. Definition of Data Journalism
  • 4.Case studies from Turkey and from other countries
  • Practice
  • 5.Assembling a team for a data journalism project
  • 6.Skills necessary for working with data
  • TELLING STORIES WITH VISUALIZATION
  • 1.Module introduction
  • 2.History of Data Visualization
  • 3.Definition of Data Visualization
  • 4. Basic Principles of Data Visualization
  • 5.Creating good data visualization
  • 6.Matching Data and Graph Types: Interpreting Data Visualizations
  • DATA VISUALIZATION TOOLS
  • 1.Module introduction
  • 2.Using Excel to do data journalism
  • 3.Data journalism basics with Google spreadsheets
  • 4.Simple visualization tools: Infogram, Piktochart, Workbenchdata, Flourish and datawrapper
  • 5.Data visualization: advanced visualizations using Tableau.
  • Resource
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Last updated 5 years ago

First we have to start with some questions. Why visualize? Why is this important? For whom? For what? What is important to show, what chart should we show and how? We also need to understand the importance of visualization. Why visualize data? It is a good way to communicate complex information.

And you should ask yourself what you want to show.

Below are some examples of things not to do when visualizing data. As long as you pay attention to these basic principles, you will pave the way for a good data visualization.

Title and axis names must be given to the graphics.

We do not prefer three-dimensional graphics!

We don't prefer Pie Charts either.

Using Data Labels In Static Images Can Be Useful.

We must pay attention to the axes! It can be misleading.

It is important to use the correct font.

Data Visualization Development Methods

  • Ensure that the correct visualization tool is selected for the data set. Choose the tool that is most comfortable to use, with the greatest chance of practice

  • Make sure that the information is presented correctly, be skeptical (the visualization tool causes distortions in the vein, misinterpretation / interpretation of the data).

  • Analyze / sort / use the information in the logic frame (categorical attention, alphabetical order, etc.) This allows the reader to communicate more with the data.

  • It must be easy to compare (easy to read / easy to filtering / comparison, easy to understand )

  • The data should be brought to the attention needed. (unnecessary icons, symbols, additional visualizations and distracting viewers)

Use appropriate color palettes and symbols (Increase the meaning It is important to remove unnecessary tags)

ColorTab
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4. Basic Principles of Data Visualization