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Research Seminar

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Кто читает:
Кафедра инноваций и бизнеса в сфере информационных технологий
Статус:
Курс обязательный
Когда читается:
2-й курс, 1-3 модуль

Course 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

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

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

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

Assessment Elements

  • non-blocking Classroom work
  • non-blocking Individual Work (self-study)
  • non-blocking Project
Interim Assessment

Interim Assessment

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

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