Infopaket

Big data in geosciences

ОК 8

Обов’язкова дисципліна для ОП

Second

2024/2025

2 Semester

3

To analyze features of natural and anthropogenic systems and objects of upper part of the Earth's crust and its sedimentary layer, in particular. To be able to communicate with specialists and experts of various degree in other branches of knowledge, including international and global information medium. To doing thematic processing and interpretation of geospatial data taken from different geological methods, to develop relating algorithms and software, to making geodatabases and web-publishing of cartographic data. To apply tools of geographic information systems for solution of actual tasks of geological branch.

Full-time form

1. Basic knowledge of general disciplines of Earth sciences (geology, geophysics, geodesy, etc.), basic of databases and programming. 2. Basic technical skills in informational technologies, programming, data processing and databases. 3. The level of English proficiency at least B1.

Academic discipline "Big data in Geosciences" that integrates the knowledge of geodata in Earth sciences is part of education and professional training program for the education level "master" branch of knowledge 10 - Natural Science of specialty 103 - Earth Sciences, educational program: Geoinformatics; This discipline is selective discipline for educational program " Geoinformatics ". The discipline is taught in the 2nd semester of 1st year Master’s degree program in volume - 90 hours (3 credits ECTS) including lectures - 14 hours, practical works - 14 hours, consultations - 2 hours, self-study work - 60 hours. The course content provides two modules and two module tests. The discipline is finished by test.

1. Cebr: Data equity, Unlocking the value of big data. in: SAS Reports, (2012) 2. EMC: Data Science and Big Data Analytics. In: EMC Education Services, (2012) 3. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: The Next Frontier for Innovation, Competition, and Productivity. In: McKinsey Global Institute Reports, (2011) 4. Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg (2011) 5. Russom, P.: Big Data Analytics. In: TDWI Best Practices Report, (2011) 6. Big Data: Survey, Technologies, Opportunities, and Challenges http://www.hindawi.com/journals/tswj/2014/712826/ 7. “Challenges and Opportunities with Big Data”, A community white paper developed by leading researchers across the United States. 8. C. Wunsch, Discrete Inverse and State Estimation Problems With Geophysical Fluid Applications. 371 pp: Cambridge University Press, 2006.

lectures, practical classes, self-studying work

For admission to the final grading it is obligatory: 1) to pass two control tests; 2) to prepare six oral reports, which can be presented in the form of presentations and abstracts. The final grading is carried out in the form of written modular test.

English