Introduction to Big Data

2019/2020

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Department of Innovation and Business in Information Technologies

Course type:

Elective course

When:

3 year, 1 module

Instructors

Prokofyeva, Elizaveta S.

Tsukanova, Olga A.

Course Syllabus

Abstract

“Introduction to Big Data” is a “blended” course taught in the 3d year of the bachelor’s program. The course consists of the on-line part provided by www.coursera.org (course title – Introduction to Big Data, https://www.coursera.org/learn/big-data-introduction) and the off-line part described below. The students are supposed to study the on-line part on their own using the materials available at www.coursera.org. The off-line part of the course helps students better understand the basics of Big Data by communicating with instructors. The coverage of the offline part is not limited to the topics of the on-line part and makes special emphasis on the topical issues of the applied fields, which may be hard for self-study. The duration of the course is one module. The course is worth 4 credits.

Learning Objectives

• The course is to introduce students to the core concepts of Big Data analysis and application to selected applied fields

Expected Learning Outcomes

• - Know the fundamental concepts, principles and approaches to description of the Big Data Landscape. - Be able to understand the main problems of the Big Data Analysis, get acquainted to the architectural components and programming models used for scalable data analysis. - Learn how to use one of the most common frameworks, Hadoop.
• The following competences: - Being able to explicate the scientific essence of problems in the professional field - Being able to use the relevant mathematical and technical tools for processing, analysis and systematization of data on the topic of research - Being able to prepare scientific reports and presentations

Course Contents

• 1. ON-LINE PHASE
  - Introduction. - Big Data: Why and Where. - Characteristics of Big Data and Dimensions of Scalability. - Data Science: Getting Value out of Big Data. - Foundation for Big Data Systems and Programming. - Systems: Getting Started with Hadoop
• 2. OFF-LINE PHASE
  Selected topics related to Big Data in: - Social Network Analysis - Industrial Internet - Healthcare

Assessment Elements

• on-line tests
• paper based exam
  The duration of the exam is 90 minutes.

Interim Assessment

• Interim assessment (1 module)
  0.6 * on-line tests + 0.4 * paper based exam

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

Recommended Additional Bibliography

• Hillyard, S., & Hand, M. (2014). Big Data? : Qualitative Approaches to Digital Research (Vol. First edition). Bingley, UK: Emerald Group Publishing Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=908919