Course Description

Many of the major challenges we grapple with today emanate from interacting and understanding our natural world: including global climate change, the disappearance of forests, outbreak of diseases and spread of invasive species, and the value derived from nature. Today, environmental and climate change significantly affect our lives. However, what is the role of data in providing us with insights regarding such changes to our world? The advancement of micro-sensors and satellites, the Internet, and informatics have led to the availability of unprecedented quantities of data regarding the natural world, thus posing challenges in synthesizing and processing large sources of information. This course will enable students to use the methods and knowledge gained to the real world environmental and ecological data sets. It is mostly a hands-on approach that will allow students to gain more insight into issues in our natural world.

Course Modules and Syllabus

• Ecological Networks

• Climate Change

• Collapse of fisheries

• Megafaunal extinctions

  This course is for people interested in gaining knowledge about the environment, renewable energy, and data science skills. It will suit those interested in data science and its applications in renewable energy and the environment. In this course, we look at and critically explore the use of data science and statistics in improving our understanding of the environment.

Learning Outcomes

• Interpret open data from citizen science.

• Gain skills for thinking critically the nature of environmental evidence and the way it is used Understand and appreciate statistical models’ nature and be able to critique statistical models for spatial and temporal data.

• Students should be able to not only understand but also implement data science for monitoring of the environment.

• Understand the nature of variability and uncertainty in environmental data.

• Use real-world environmental data and programming language R to complete modeling activities

One (1) Term  = Four Weeks

Note: Three weeks online study and one week for project work.

Tuition and Certification

Payment