Learn how data science is used for climate research by evaluating promising changes in the data infrastructure techologies that can now better support climate and ecosystem simulations. Jungwoo will introduce initiatives trying to take advantage of these advacnements and analyze the type of data being fed into the predictive models used by the simulations.
- [Voiceover] One of the areas where simulations can be used is predictive modelling, 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 analytics, explores which markets are using big data the most, 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 four 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 analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data