Applications 2022-12-05
COURSE DESCRIPTION
Big data and the algorithms used in data science, together with the corresponding process and its technology tools, have important implications for addressing climate change. From machine learning algorithms to data visualization, data science methods are used to investigate and better understand climate change and its various effects on land, sea, food, etc.
Data science is a powerful approach which is capable of helping practitioners, and policy-makers understand the uncertainties and ambiguities inherent in data, to identify interventions, strategies, and solutions that realize the benefits for humanity and the environment, and to evaluate the multiple– and sometimes conflicting–goals of decision-makers. In this MOOC course, we introduce methods pertaining to the growing field of data science and apply them to issues relevant to climate change.
Topics
Course content
You will learn
By the end of the course, you will be able to: obtain and analyze datasets; make data-driven decisions; identify and address climate change challenges using data science
Who is the course for?
This course is designed for those who want to improve their analytics and data-driven decision-making skills, with an emphasis on utilizing such skills for addressing climate change challenges. The course will also be useful for practitioners and policy-makers as they can benefit from understanding the uncertainties and ambiguities inherent in data and using it to identify interventions, strategies, and solutions that realize benefits for humanity and the environment.
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling
In this course you will learn state-of-the-art statistical modelling for the purpose of analysing industrial data. The course also presents the basics of relational databases and data manipulation techniques needed to prepare the data for analysis.
This course provides an understanding of the fundamental problems in software testing, as well as solid foundation in the practical methods and tools for a systematic state-of-the-art approach to testing of software.
Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. The course is part of the education initiative Smarter at Örebro University. This is course requires completion of course Reinforcement Learning part 1. Read more
The manufacturing industry collects increasingly large volumes of big data, that is, data at high speed, generated from a wide range of sources in different formats and quality levels. But what is data without insight? This course will help you master the fundamental concepts of big data, cloud computing and smart decision-making for industrial analytics. Designed specifically for manufacturing sector professionals, this Master’s course provides knowledge and insights in handling and processing data, using machine learning and data analytics in the cloud environment. You will learn machine learning-based solutions for industrial applications, such as smart decision-making and predictive maintenance, using state of the art cloud platform tools.