Course Outline and Learning Outcomes

Let’s look at an overview of the course content for each week.

Week one: The first week of this course will give you an introduction to data science. You will be introduced to subjects such as “the Data Flood” and its sources, big data and how data can be managed. You will also begin to explore algorithms and different types of analytics, which are two very important elements of data science.

Week two: In the second week of this course, you will learn more about visualisation and analytics as a process. You will be introduced to different ways of visualising information, and the cross industry standard data mining process, CRISP-DM. We will also touch upon other processes such as Team Data Science, and introduce the software Orange, which you will be asked to download and install for use in an exercise.

Week three: In the third week of this course, you will be introduced to data-driven decision-making. You will evaluate your own decision making, as well as explore other processes such as the Simon Process and the Garbage Can model. In this week, you will also discuss the collaboration between human decision makers and algorithms, and the challenges this might include.

Week four: In the fourth and final week of this course, you will learn how data science and climate change is connected, and how they relate to the UN Sustainable Development Goals. You will be given some examples on how data science and analytics can be used to achieve the SDGs and to solve different climate change problems. In the last activity, we celebrate your success and summarize some key takeaways from the course. You will also be given a few tips on where to find more information on the subject.

Learning outcomes

After completing this course, we think that you will be able to:

• Evaluate and analyze datasets
• Apply knowledge about data science to make data-driven decisions
• Identify and assess climate change issues related to data science activities