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Course: Big Data 2016

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* '''Lab:''' Hands-on Hadoop (HPC)
* '''Lab:''' Hands-on Hadoop (HPC)
* '''Programming assignment:''' Map Reduce (check NYU Classes)
* '''Programming assignment:''' Map Reduce (check NYU Classes)
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* '''Readings''':
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** Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
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** Data-Intensive Text Processing with MapReduce (Jan 27, 2013), Chapter 6 -- Processing Relational Data (this chapter appears in the 2013 version of the textbook -- http://lintool.github.io/MapReduceAlgorithms/ed1n/MapReduce-algorithms.pdf)
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== Week 7 - March 7: Parallel Databases vs MapReduce; Introduction to SPARK==  
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== Week 7 - March 7: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK==  
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* Lab: Hands-on SPARK (HPC)
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*''' Lecture notes:''' ** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/paralleldb-vs-hadoop.pdf
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* Programming assignment: check NYU Classes on March 10th
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* '''Lab:''' NoSQL
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* '''Programming assignment:''' Pig and Spark
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* '''Readings''':
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** Benchmark DBMS vs MapReduce (2009): http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf
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** MapReduce: A Flexible Data Processing Tool: http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
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* '''Additional Suggested reading:'''
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** Hive - A Warehousing Solution Over a Map-Reduce Framework. http://dl.acm.org/citation.cfm?id=1687609
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** Pig Latin: A Not-So-Foreign Language for Data Processing. http://dl.acm.org/citation.cfm?id=1376726
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** BigTable: http://fcoffice.googlecode.com/svn/%E4%B9%A6%E7%B1%8D/bigtable-osdi06.pdf
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** Spark: Cluster Computing with Working Sets. http://static.usenix.org/legacy/events/hotcloud10/tech/full_papers/Zaharia.pdf
== Week 8 -- March 14th: Spring Break ==
== Week 8 -- March 14th: Spring Break ==

Revision as of 16:01, 23 January 2016

Contents

DS-GA 1004- Big Data: Tentative Schedule -- subject to change

  • TAs:
    • Yuan Feng
    • Kevin Ye
  • Lecture: Mondays, 4:55pm-7:35pm at 19 University Pl., room 102.
  • Some classes will include a lab session, please always bring your laptop.

News

Week 1 - Jan 25: Course Overview

Week 2 - Feb 1: The evolution of Data Management and introduction to Big Data; Introduction to Databases, Relational Model and SQL

Week 3 - Feb 8: Introduction to Databases, Relational Model and SQL (cont.)

Week 4 - Feb 15: Holiday

Big Data Foundations and Infrastructure (3 weeks)

Week 5 - Feb 22: Introduction to Map Reduce

Week 6 - Feb 29: MapReduce Algorithm Design Patterns

Week 7 - March 7: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK

Week 8 -- March 14th: Spring Break

Transparency and Reproducibility (1 week)

Week 9 - March 21: Data Exploration and Reproducibility

  • Programming assignment 4: Exploring urban data (see NYU Classes)

Big Data Algorithms, Mining Techniques, and Visualization (6 weeks)

Week 10 - March 28th: Finding similar items

  • Homework Assignment
    • See quizzes on Gradiance -- Distance measures and document similarity.

Week 11 - April 4th: Association Rules


  • Suggested additional reading:
    • Fast algorithms for mining association rules, Agrawal and Srikant, VLDB 1994.
    • Data Mining Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann
    • Dynamic Itemset Counting and Implication Rules for Market Basket Data. Brin et al., SIGMOD 1997. http://www-db.stanford.edu/~sergey/dic.html
  • Homework Assignment
    • See quizes on Gradiance -- Distance measures and document similarity.

Week 12 - April 11th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CUSP)

Week 13 - April 18th: Parallel Databases

Week 14 - April 25th: Graph Analysis

  • Required Reading: Data-Intensive Text Processing with MapReduce. Chapters 5 -- Graph Algorithms

Week 15 - May 2: Final Exam

Week 16 - May 9: Project Presentations

Week 17 - May 16: Project Presentations

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