Advanced Data Visualization

Concise and informative visualisation is the key for comprehension and communication of data and analysis results. Especially these days where the amount of data is often huge and traditional visualisation forms may not be suitable. Treatments like data reduction or aggregation are then needed to visualise the data efficiently.

Visualisation of data plays an important role in data science, machine learning and analysis platforms. Raw data needs to be inspected, results of computational simulations and algorithms need to verified, and the end-users need insight into the data too. Given the large amounts of data available in today’s analyses, implementing efficient and responsive visualization software is far from trivial.

Our consultants have expertise in standard and advanced data visualisation techniques. This includes pretreatments such as reduction, aggregation and contextualising of data. For more complex visualization of larger data sets, a combination of different techniques is often required. For instance, deriving some statistical measures across spatial, temporal and parametric dimensions can be combined with blending techniques to provide novel visual insight.

Consultants:

Daniel Marelius Bjørnstad

Daniel is a seasoned software developer with a PhD in neuroscience, specializing in data analysis and cloud software development. With expertise in multiple stages of project development, Daniel’s work spans from the initial definition of project goals and value propositions to the deployment of final products, including services, dashboards, reports, production models, and APIs. His background in neuroscience provides a strong foundation in complex data processing and analysis, allowing him to tackle challenging problems in diverse domains. He started working professionally with software development in 2016.

Diako Darian

Diako holds a Ph.D. in Computational Mathematics from the University of Oslo. As a student at both Mathematics and Physics departments at the University of Oslo, he has acquired a broad knowledge in various physical and mathematical theories, and numerical methods.

Guttorm Kvaal

Guttorm is a curious person with an interest and enthusiasm for technology and problem solving. His academic background is focused around applied mathematics, programming, and physics. He submitted his master’s thesis the summer 2017 in the field of Computational Science and Engineering at the University of Oslo / Simula.

Kine Onsum Moseid

Kine is an organized scientific programmer that likes to solve problems from a big picture approach. She submitted her PhD thesis in Climate Science in December of 2021, and during her doctorate she analyzed radiation and air pollution data from a multitude of climate model simulations and compared them with observations. This work gave insight in big data analytics and visualisation, especially regarding time series and how to avoid common pitfalls like comparing apples and oranges. She enjoys working with large datasets, learning new things, and communicating/teaching science.

Ola Skavhaug

Founder and CEO of Expert Analytics. Loves mixing high and low-level languages to combine flexibility with performance.

Robert Solli

Robert Solli is a specialist in scientific programming, with expertise in mathematical optimisation, statistical analysis and machine learning. He has solved a varied set of problems with this skillset, from understanding complex physical systems to increasing student volunteer participation.

Sebastian Franco Ulloa

Sebastian is a curious person always looking to learn about new topics and combining them into novel solutions to complex problems. He received his PhD in biocomputional sciences in March 2021 from the Italian Institute of Technology/University of Bologna, where he studied how nanomaterials interact with physiological environments. He is experienced in molecular simulations, data visualization, and scientific writing. In more recent years, Sebastian has been applying bioinformatic methods to genomics, proteomics, and transcriptomics data.