Research Seminar

Master 2020/2021

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course

Area of studies: Business Informatics

Delivered by: Department of Innovation and Business in Information Technologies

Where: Graduate School of Business

When: 2 year, 1-3 module

Mode of studies: offline

Instructors: Petr Baranov, Natalya Khapayeva, Petr Panfilov

Master’s programme: Big Data Systems

Language: English

ECTS credits: 6

Contact hours: 72

Full Syllabus

Abstract

The objective of the Research seminar is to elaborate skills and experience in development work in the process of students’ preparation for theses and graduate qualification papers (master’s thesis) of the master’s program “Big Data Systems”. The course focuses on the research of the areas of technologies and big data appliance, study of the practical work with instrumentation of Big Data, as well as the analysis of the development of technologies. The main purpose of the Research seminar is to develop academic competences in analysis and evaluation of the impact of new information technologies, including Big Data and related technologies on business performance and its architecture, as well as best practices of the Big Data technologies. The final goal of the Seminar is to make student's scientific activities being permanent and systematic element of the educational process, to include students into the life of typical scientists, to help learning methodology, technology and tools for research activity.

Learning Objectives

Training students skills in an academic work, including preparation and carrying out scientific projects, writing scientific papers
Training scientific discussion and presentation of ideas, concepts, research results, projects and research papers
Training the use of Big Data technologies for scientific activities
Training methods and skills in scientific forecasting for definition of technological trends in the field of information technologies

Expected Learning Outcomes

Uses big data technology to address the challenges of building an organization's information infrastructure
Uses architectural solutions based on big data
Assesses the cost-effectiveness of big data management solutions
Understands and evaluates the prospects for the development of functionality and applications of Big Data technologies

Course Contents

Mathematical and technological basis of big data tooling
The research into the peculiarities of appliance technologies to the tasks, connected with the formation of information infrastructure of an organization, new opportunities for analytics and decision-taking
Architectural solutions on the basis of big data for enterprises
The transformation of the system of data managing, formation of information assets of an enterprise, systems of data collection about business processes, elaboration of external data, new design of cooperation, system interaction
Big data economics
The evaluation of economic efficiency of solutions for enterprise management on the basis of big data technologies, possibilities to use information assets of enterprises, opportunities to use solutions on the basis of unstructured information from various sources in the enterprise administration
Prospects for the development of functionality and spheres of Big Data technology appliance

Assessment Elements

Classroom work
Individual Work (self-study)
Project

Interim Assessment

Interim assessment (3 module)
0.4 * Classroom work + 0.3 * Individual Work (self-study) + 0.3 * Project

Bibliography

Recommended Core Bibliography

Berman, J. J. (2018). Principles and Practice of Big Data : Preparing, Sharing, and Analyzing Complex Information (Vol. Second Edition). London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1731816
Big data in economics: evolution or revolution? (2017). United Kingdom, Europe: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.5BD0A2C5
Bruce, P. C., & Bruce, A. (2017). Practical Statistics for Data Scientists : 50 Essential Concepts (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1517577
Buyya, R., Calheiros, R. N., & Vahid Dastjerdi, A. (2016). Big Data : Principles and Paradigms. Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1145031
Claudio Feijóo, José-Luis Gómez-Barroso, & Shivom Aggarwal. (2016). Economics of big data. Chapters, 510. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.h.elg.eechap.14700.25
Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1–6. https://doi.org/10.1126/science.1243089
Krish Krishnan. (2019). Building Big Data Applications. [N.p.]: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892146
Liu, S., Xie, Y., Ge, Z., & McGree, J. (2016). Computational and Statistical Methods for Analysing Big Data with Applications. London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1102854
Mahmood, Z. (2016). Data Science and Big Data Computing : Frameworks and Methodologies. Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1203573
Mihai Andronie, Daniel Adrian Gârdan, Ionel Dumitru, Iuliana Petronela Gârdan, Irina Elena Andronie, & Cristian Uță. (2019). Integrating the Principles of Green Marketing by Using Big Data. Good Practices. Amfiteatru Economic, (50), 258. https://doi.org/10.24818/EA/2019/50/258
Prabhu, C. S. R. (2019). Fog Computing, Deep Learning and Big Data Analytics-Research Directions. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1994845

Recommended Additional Bibliography

Dalla Vecchia, R. (2015). The Relationship between Big Data and Mathematical Modeling: A Discussion in a Mathematical Education Scenario. Themes in Science and Technology Education, 8(2), 95–103. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1131006
Dr. Tony McCaffrey. (2018). Limits to a Computer’s Creativity: Mathematical Proof and Consequences for Big Data. https://doi.org/10.5281/zenodo.1184975
Loyiso G. Nongxa. (2017). Mathematical and statistical foundations and challenges of (big) data sciences. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.D5123A28
Müller, O., Fay, M., & vom Brocke, J. (2018). The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. Journal of Management Information Systems, 35(2), 488–509. https://doi.org/10.1080/07421222.2018.1451955
Ning, C., & You, F. (2019). Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming. https://doi.org/10.1016/j.compchemeng.2019.03.034
Raheem, N. (2019). Big Data : A Tutorial-Based Approach (Vol. First edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2031482
Sarangi, S., & Sharma, P. (2020). Big Data : A Beginner’s Introduction. Abingdon, Oxon: Routledge India. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2168187

Course Syllabus