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Analyzing Data

An article with more detailed information about how to analyze the data that is out there in the world.

Earlier, in activity three, we introduced different data sources such as the European Commission and Statista. In this article, we attempt to analyze one of them. Datasets of different formats can be used, we recommend XLS which is what will be provided in this course.

Remember, to analyze the data, we need a tool. Earlier this week, we introduced Orange as the tool to be used in this course. We encourage you to create an account and perform this exercise. If you are unable to perform the exercise, please follow along the process below as best you can.

Steps to analyse the dataset

  • Open Orange on your computer.
  • Download the data. You can access the dataset here.
  • Open the downloaded excel sheet (the dataset downloaded as an XLS-file), and Click on the sheet called “Data”
  • Since we only want to analyze the value, highlight and delete the first four rows.
  • In the empty row add “Year” and “Emissions” as row headers.
  • Now, remove column A (right-click, delete) as it has empty values.
  • The sheet changes from what we see first, to what we see on the second.
  • Go back to Orange, right-click on the white area to get the pop-up menu, write “File” and click so that it appears in the white area. Likewise, right-click and then select Scatter plot then click on it.
  • Connect the File to the Scatter Plot widgets.
  • Double-click on File and locate the data file, then select the datasheet.
  • Save under the name “Task1” by clicking on the File menu.

Now, your canvas should look something like the following:

  • Double-click on the Scatter Plot, and make sure you have selected the right parameters on the left pane (for example Axis x, Axix y, etc.).

examples canvas

There are several ways to interpret this graph. For example, Global carbon dioxide (CO2) emissions from fossil fuels and industry have increased considerably since 2000, and in 2019 reached a record high of 36.7 billion metric tons of CO2. In 2020, the COVID-19 pandemic caused global CO2 emissions to plummet five percent to 34.81 billion metric tons. It is projected that emissions rebounded in 2021 as lockdowns ease.

What have you learned?

  1. We need data
  2. The data needs preprocessing
  3. The analysis has just started
© Luleå University of Technology
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Data Science for Climate Change

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