Learn how data science is used for climate research by evaluating promising changes in the data infrastructure technologies that can now better support climate and ecosystem simulations. Explore initiatives trying to take advantage of these advancements a
- [Voiceover] One of the areas where simulations can be used is predictive modeling, powered by data science. Among its many applications, climate and ecosystem change predictions stand out as one of the most timely and significant way of harnessing the power of data science. For example, there is the United Nations initiative called Data for Climate Action Challenge. It's a competition aimed at encouraging climate and data scientists to develop innovative climate change research projects, by leveraging data analytics.
Going a little further, now it's no longer a pipe dream to simulate the entire ecosystem of the Earth. The Madingley Model project, sponsored by Microsoft, is making this dream a reality. Using the Madingley Model, scientists can simulate the impact of climate changes on all lifeforms on Earth. The data fed into these predictive models of climate changes and ecosystems include environmental data reported through social media and sensor readings coming from various Internet of Things, or IoT devices, as well as conventional climate data.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why data analytics is important
- How data science is used in fraud detection, disease control, network security, and other fields
- Data science skills
- Data science roles
- Data science certifications
- The future of data science
Skill Level Beginner
Insights on Data Science: Lillian Piersonwith Lillian Pierson, P.E.23m 51s Intermediate
Learning Data Science: Understanding the Basicswith Doug Rose1h 16m Appropriate for all
1. Define Data Science
6. Future of Data Science
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.