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Regular version of the site

Big Data

2018/2019
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
1 year, 3 module

Instructor


Gilev, Aleksey

Course Syllabus

Abstract

The course is for students new to data science and interested in understanding why the Big Data Era has come to be. It is for students who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible.
Learning Objectives

Learning Objectives

  • The main goal of this course is to get students familiar with Big Data
Expected Learning Outcomes

Expected Learning Outcomes

  • Student is introduced to notion of big data
  • Student is familiar with characteristics of big data and dimensions of scalability
  • Student knows the value of big data
  • Student is familiar with foundations for big data systems and programming
  • Student is familiar with Hadoop
Course Contents

Course Contents

  • Introduction
  • Big Data: Why and Where
  • Characteristics of Big Data and Dimensions of Scalability
  • Data Science: Getting Value out of Big Data
  • Foundations for Big Data Systems and Programming
  • Systems: Getting Started with Hadoop
Assessment Elements

Assessment Elements

  • non-blocking Tests
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.35 * Exam + 0.65 * Tests
Bibliography

Bibliography

Recommended Core Bibliography

  • Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data. (2014). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.21586B3D

Recommended Additional Bibliography

  • Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485. https://doi.org/10.1126/science.aal4321
  • King, G. (DE-588)135604311, (DE-576)166299405. (1994). Designing social inquiry : scientific inference in qualitative research / Gary King; Robert O. Keohane; Sidney Verba. Princeton, NJ: Princeton Univ. Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.039730549