Andhra Pradesh State Council of Higher Education B.Sc. Computer Science/Information Technology (IT) Syllabus Under CBCS w.e.f.2015-2016 (Modified in April 2016) Structure of Computer Science/Information Technology (IT) Syllabus UNIT IV Introduction distributed file system algorithms using map reduce, MAP REDUCE: – – Matrix-Vector Multiplication by Map Reduce Hadoop - Understanding the Map Reduce – architecture - Writing Hadoop Map Reduce Programs - Loading data into HDFS - Executing the Map phase - Shuffling and sorting - Reducing phase execution. UNIT V DELIVERING RESULTS :Documentation and deployment producing effective – presentations– Introduction to graphical analysis plot() function displaying – – multivariate data matrix plots multiple plots in one window - exporting graph - using – – graphics parameters. Case studies. Reference Books 1.Nina Zumel, John Mount, Data Science with R”, Manning Publications, 2014. “Practical 2.Jure Leskovec, Anand Rajaraman, Jeffrey D.Ullman, of Massive Datasets”, “Mining Cambridge University Press, 2014. 3.Mark Gardener, R - The Statistical Programming Language”, John “Beginning & Sons, Inc., 2012. 4.W. N. Venables, D. M. Smith and the R Core Team, Introduction to R”, 2013. 5.Tony “An Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, Data “Practical Science Cookbook”, Packt Publishing Ltd., 2014. 6.Nathan Yau, This: The FlowingData Guide to Design, Visualization, “Visualize and Statistics”, Wiley, 2011. 7.Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Hadoop “Professional Solutions”, Wiley, ISBN: 9788126551071, 2015. Student Activity: Collect data from any real time system and create clusters using any clustering algorithm Read the student exam data in R perform statistical analysis on data and print results. Page 32 of 50
Andhra Pradesh State Council of Higher Education B.Sc. Computer Science/Information Technology (IT) Syllabus Under CBCS w.e.f.2015-2016 (Modified in April 2016) Structure of Computer Science/Information Technology (IT) Syllabus UNIT IV MAP REDUCE: Introduction – distributed file system – algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce – Hadoop - Understanding the Map Reduce architecture - Writing Hadoop Map Reduce Programs - Loading data into HDFS - Executing the Map phase - Shuffling and sorting - Reducing phase execution. UNIT V DELIVERING RESULTS :Documentation and deployment – producing effective presentations– Introduction to graphical analysis – plot() function – displaying multivariate data – matrix plots – multiple plots in one window - exporting graph - using graphics parameters. Case studies. Reference Books 1.Nina Zumel, John Mount, “Practical Data Science with R”, Manning Publications, 2014. 2.Jure Leskovec, Anand Rajaraman, Jeffrey D.Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2014. 3.Mark Gardener, “Beginning R - The Statistical Programming Language”, John & Sons, Inc., 2012. 4.W. N. Venables, D. M. Smith and the R Core Team, “An Introduction to R”, 2013. 5.Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, “Practical Data Science Cookbook”, Packt Publishing Ltd., 2014. 6.Nathan Yau, “Visualize This: The FlowingData Guide to Design, Visualization, and Statistics”, Wiley, 2011. 7.Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 2015. Student Activity: Collect data from any real time system and create clusters using any clustering algorithm Read the student exam data in R perform statistical analysis on data and print results. Page 32 of 50
BSc_Comp_SCi_IT_syllabus_final.docx