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Regular version of the site
Master 2019/2020

Introduction to Big data

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Elective course (Complex Social Analysis)
Area of studies: Sociology
Delivered by: School of Sociology
When: 2 year, 1 module
Mode of studies: Blended
Instructors: Alexander Byzov
Master’s programme: Complex Social Analysis
Language: English
ECTS credits: 3

Course Syllabus

Abstract

This is a blended course "Introduction to Big Data". The goal of this course is to help a student to get familiar with Big Data (what is Big Data, what are characteristics of it, how could you analyze such data etc.). Link to the online course: https://www.coursera.org/learn/big-data-introduction
Learning Objectives

Learning Objectives

  • Study of Basic notions of Big Data research
Expected Learning Outcomes

Expected Learning Outcomes

  • Describe the Big Data landscape including examples of real world big data problems and approaches
  • Explain the V’s of Big Data and why each impacts the collection, monitoring, storage, analysis and reporting, including their impact in the presence of multiple V’s.
  • Identify big data problems and be able to recast problems as data science questions
  • Summarize the features and significance of the HDFS file system and the MapReduce programming model and how they relate to working with Big Data
  • Know basic concepts of Big data, its opportunities, limitations, and relevance to social sciences;
Course Contents

Course Contents

  • Introduction to Big Data in Sociological Research
    Big data applications in various types of social studies. Cases. Biases. Ethical concerns.
  • Big Data Foundamentals
    Objectives of the course. "The launch of Big Data era". Big Data applications. Sources of Big Data.
  • Characteristics of Big Data and Dimensions of Scalability
    Variety, velocity, veracity, and valence of Big Data. Analysis of Big Data.
  • Foundations for Big Data Systems and Programming. Hadoop
    Key concepts of Big Data programming. Hadoop. MapReduce.
Assessment Elements

Assessment Elements

  • non-blocking Test
    The test to this course is a short essay (half of a page or more) based on the topics of the course
  • non-blocking FInal grade in online course "Introduction to Big Data" on Coursera
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.3 * FInal grade in online course "Introduction to Big Data" on Coursera + 0.7 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Chu, W. W. (2013). Data Mining and Knowledge Discovery for Big Data : Methodologies, Challenge and Opportunities. Heidelberg: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=643546
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data : A Revolution That Will Transform How We Live, Work, and Think. Boston: Eamon Dolan/Houghton Mifflin Harcourt. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1872664

Recommended Additional Bibliography

  • Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. I. (2017). Big Data and Social Science : A Practical Guide to Methods and Tools. Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1353316