The programme has been created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). Also taken into consideration when creating the faculty was the School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. In the faculty, learning is based on practice and projects.
This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials. More specifically, it is argued that modern data sciences (including advanced statistics, dimensionality reduction, and formulation of metamodels) and innovative cyberinfrastructure tools (including integration platforms, databases, and customized tools for enhancement of collaborations among cross-disciplinary team members) are likely to play a critical and pivotal role in addressing the above challenges.
"Data scientist" has already been declared this year's hottest job, and now a new report offers several more reasons to consider it as a career. For the past three years executive recruiter Burtch Works has been surveying data-science professionals about salaries and other related topics. Burtch Works defines data scientists as professionals who can work with enormous sets of unstructured data and use analytics to get meaning out of them. Published on Thursday, this year's report is based on interviews with 374 working data scientists, and it paints a pretty compelling picture. Here are five particularly attractive highlights.
I started to get interested in data science when I finished one of my first MOOC courses, Data Analysis and Statistical Inference. It is a great course, which introduced me to statistics and R, the programming language for data science. Since then, I haven't stopped. I continued with Data Science specialization on Coursera and The Analytics Edge on Edx, both inspired me and eventually lead me to the world of machine learning. I was so amazed that I, as a chemist, decided to change my career path and start over as a data scientist.