In a data-driven world, it is important to be able to analyze large amounts of data to identify patterns of interest and test hypotheses about them. Visual Analytics provides us with an interactive process of analytical reasoning facilitated by data visualization, combining the strengths of humans and computers in order to derive insight from massive, dynamic, ambiguous, and often conflicting data. In this course we will introduce basic concepts of data visualization, how to apply them to build interactive interfaces for data sets of different types, and which tools are useful in this process. Target group This course is for experienced developers working in the industry with an interest in data analysis and visualization. Content Foundations of perception and design that are important for creating new visualizations.Comparison between different types of visualization that work better for different types of data.Integration of multiple individual visualizations into interactive dashboards.Overview of the exploratory visual analysis process that incorporates all the above into a unified pipeline.Practical applications using interactive visualization libraries. Practical information All materials will be available digitally, including reading materials, lecture slides, videos, practical exercises, etc. The course will be given in a flexible manner to facilitate the combination of course work with your professional commitments. We recommend that you work on a project during the course that you can use in your daily work, with your own data, and your own problems. Entry requirements The basic eligibility for this course is a Bachelor degree. Candidates with relevant work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at the Bachelor level.
Numerical models are used in every engineering task, from conceptual design to optimization, control, and diagnostics. As the process becomes more complex, data driven models are a powerful tool that allows to quantify relationships between available data and observations, which forms the basis for machine learning. Image recognition, spam filtering, and predictive analytics are some examples of how we can use data driven models. This course provides a simple introduction to fundamental techniques for dimensionality reduction, classification, and regression, which can be applied to all types of engineering problems.
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 Data science Analytics as a process Data-driven decisions Climate change Applications of data science in climate change Course content Understand data science Learn about the sources of big data Understand the basics of climate change, its impacts and sustainable development goals Get to know data-driven decisions and how they are made Highlight some climate change challenges that are directly or indirectly related to data science Apply data science knowledge and skills to make climate change related decisions Learn how others have used data science in association with addressing climate change problems You will learnBy 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 information and communication technology (ICT) sector is responsible for approx. 1.8-2.8% of the global greenhouse gas (GHG) emissions in 2020, and software is both part of the problems and the solutions. Traditional software engineering principles and techniques do not consider the climate, environment, and sustainability aspects in building and using software for any purpose. We, software engineers, developers, researchers, climate scientists, and various other related stakeholders, need to think about how we can reduce the carbon footprint due to building and using software-intensive systems. Green and sustainable software engineering is an emerging concept that can help reduce the carbon footprint related to software. In this introductory course, we will introduce the concept of green and sustainable software engineering and the engineering process to build green and sustainable software. Topics Sustainable and green computing Sustainable and green software engineering Process Energy efficient computing Sustainability issues in Scientific computing You will learnBy the end of the course, you will be able to: analyze the green and sustainability issues in traditional software engineering, identify and incorporate key elements to be included in the software engineering process to make the software green and sustainable, and use techniques to make your software code energy efficient. Who is the course for?This course is designed for those who are software developers, managers and software related policy makers, or have knowledge about software development, and want to consider the green and sustainability aspects in their everyday life. Also, this course will be useful for computational scientists who build green software and want to know more about these aspects in software engineering. However, this is an introductory course, and it will show a path for life-long learning to build more in-depth knowledge in each concept introduced in this course.
Course in English. The course provides an introduction to self-service businessintelligence. The course focuses on increasing awareness around managing challenges when implementing and using self-service business intelligence. The assignmentscontain an account and discussion to be able to succeed with an investment within self-service businessintelligence. See Introduktion till self-service business intelligence A1N for more information.