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
Where:
Faculty of Social Sciences
When:
2 year, 1 module
Mode of studies:
distance learning
Instructors:
Alexander Byzov
Master’s programme:
Complex Social Analysis
Language:
English
ECTS credits:
3
Contact hours:
2
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
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
- Introduction to Big Data in Sociological ResearchBig data applications in various types of social studies. Cases. Biases. Ethical concerns.
- Big Data FoundamentalsObjectives of the course. "The launch of Big Data era". Big Data applications. Sources of Big Data.
- Characteristics of Big Data and Dimensions of ScalabilityVariety, velocity, veracity, and valence of Big Data. Analysis of Big Data.
- Foundations for Big Data Systems and Programming. HadoopKey concepts of Big Data programming. Hadoop. MapReduce.
Assessment Elements
- TestThe test to this course is a short essay (half of a page or more) based on the topics of the course
- FInal grade in online course "Introduction to Big Data" on Coursera
Interim Assessment
- Interim assessment (1 module)0.3 * FInal grade in online course "Introduction to Big Data" on Coursera + 0.7 * Test
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