Course: Big Data 2016

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

• Course Web page: http://vgc.poly.edu/~juliana/courses/BigData2016

• Instructor: Professor Juliana Freire (http://vgc.poly.edu/~juliana)

• 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

• 1/25/2016: Amazon has kindly donated time on AWS for all the student in this class. To obtain your credit, please follow the instructions at http://www.vistrails.org/index.php/AWS_Setup
• 1/25/2016: Access you NYU HPC account, which you will use for in-class exercises and homework assignments. See instructions at NYU Hadoop

Background (2 weeks)

Week 1 - Feb 2: Course Overview; The evolution of Data Management and introduction to Big Data

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/course-overview.pdf
• Reading: Chapter 1 of Mining of Massive Data Sets (version 1.1)
• Course survey: https://docs.google.com/forms/d/1LTiJwkDVvp0cF62Fw_d9Y86US5LCkorRUIQtV2T8KWE/viewform?usp=send_form

Week 2 - Feb 9: Introduction to Databases, Relational Model and SQL

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/intro-to-db.pdf
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/relational-algebra.pdf
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/sql-intro.pdf
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/sql-more.pdf

• Lab:
  • SQL hands on: Big Data 2015 - SQL Lab

• Other useful reading:
  • Greenspun's SQL for Web Nerds Intro
  • SQL/Nerds Modeling (parts)

• Programming assignment: Using SQL for data analysis and cleaning (see NYU Classes)

Big Data Foundations and Infrastructure (3 weeks)

Week 3 - Feb 23: Introduction to Map Reduce

• Lab: (continuation)
  • SQL hands on: Big Data 2015 - SQL Lab
• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/mapreduce-intro.pdf
• Required Reading:
  • Data-Intensive Text Processing with MapReduce. Chapters 1 and 2
  • Mining of Massive Datasets (v 2.1). Chapter 2 - 2.1, 2.2, and 2.3
• Other useful reading:
  • Hadoop: The Definitive Guide. http://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/1449311520

• Quiz 1 (Map Reduce) assigned -- check http://www.newgradiance.com/services

Week 4 - March 2: Algorithm Design for MapReduce: Relational Operations

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/mapreduce-algo-design-relations.pdf

• Lab: Hands-on Hadoop (local)

• Required reading:
  • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
  • Mining of Massive Datasets (2nd Edition), Chapter 2.

• Programming assignment: Map Reduce (check NYU Classes)

Week 5 - March 9: MapReduce Algorithm Design Patterns; Parallel Databases vs MapReduce

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/mapreduce-algo-design-patterns.pdf

• Lab: Hands-on Hadoop on AWS
  • Lab materials: http://bigdata.poly.edu/~tuananh/files/awscli-examples.zip
  • Install aws command-line interface: http://docs.aws.amazon.com/AWSEC2/latest/CommandLineReference/set-up-ec2-cli-linux.html

• Some links to AWS CLI documentation:
  • http://docs.aws.amazon.com/AWSEC2/latest/CommandLineReference/set-up-ec2-cli-linux.html
  • http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-set-up.html
  • http://www.linux.com/learn/tutorials/761430-an-introduction-to-the-aws-command-line-tool
  • EMR Through Commandline: https://www.safaribooksonline.com/library/view/programming-elastic-mapreduce/9781449364038/ch04.html
  • Importing Key: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html#how-to-generate-your-own-key-and-import-it-to-aws
  • EMR Job Flow: http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/EMR_CreateJobFlow.html

• Required reading:
  • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
  • 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)

• Programming assignment: check NYU Classes on March 10th

Transparency and Reproducibility (1 week)

Week 6 - March 23: Data Exploration and Reproducibility

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/data-science-reproducibility.pdf

• Lab: Hands-on reproducibility. Before class, please
  • Download VisTrails 2.1.5 from here: http://www.vistrails.org/index.php/Downloads
  • Download the mta-analysis example: http://bigdata.poly.edu/~fchirigati/mda-class/mta-analysis.vt
  • Download the links for the input data: http://bigdata.poly.edu/~fchirigati/mda-class/mta-links.txt
  • http://bigdata.poly.edu/~fchirigati/mda-class/hands-on.pdf
  • Questions? Email Fernando at fchirigati@nyu.edu

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

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

Week 7 - March 30th: Finding similar items

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/similarity.pdf

• Reading: Chapter 3 Mining of Massive Datasets

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

Week 8 - April 6th: Association Rules

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/association-rules.pdf

• Reading: Chapter 6 Mining of Massive Datasets

• 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 9 - April 13th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CUSP)

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/SpatialQP.pdf

• Lab: Using Amazon AWS to analyze and visualize taxi data
  • https://github.com/ViDA-NYU/aws_taxi

Week 10 - April 20th: Parallel Databases

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/paralleldb-vs-hadoop-2015.pdf

• Required reading:
  • Benchmark DBMS vs MapReduce (2009): http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf
  • MapReduce: A Flexible Data Processing Tool: http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext

• Suggested reading:
  • Hive - A Warehousing Solution Over a Map-Reduce Framework. http://dl.acm.org/citation.cfm?id=1687609
  • Pig Latin: A Not-So-Foreign Language for Data Processing. http://dl.acm.org/citation.cfm?id=1376726
  • BigTable: http://fcoffice.googlecode.com/svn/%E4%B9%A6%E7%B1%8D/bigtable-osdi06.pdf
  • Spark: Cluster Computing with Working Sets. http://static.usenix.org/legacy/events/hotcloud10/tech/full_papers/Zaharia.pdf

Week 11 - April 27th: Graph Analysis

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/graph-algos.pdf

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