Course: Big Data 2016

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

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

• Instructors:
  • Professor Juliana Freire (http://vgc.poly.edu/~juliana)
  • Dr. Erin C Carson
  • Dr. Nicholas Knight

• 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

• 1/25/2016: Amazon has kindly donated time on AWS for all the student in this class. You must signup for the AWS Educate program, see http://www.vistrails.org/index.php/AWS_Setup
• 1/25/2016: An HPC account has been created for you. You will need this account for in-class exercises and homework assignments. See NYU HPC Access Instructions

Week 1 - Jan 25: Course Overview

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/course-overview.pdf
• Lab: Computing infrastructure for the course
• 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 1: The evolution of Data Management and introduction to Big Data; Introduction to Databases and Relational Model

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/data-management-evolution.pdf
  • http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/intro-db.pdf
• Lab: getting started with MySQL
• Required Reading:
  • Chapter 1 of Mining of Massive Data Analysis
• Suggested Reading:
  • Greenspun's SQL for Web Nerds Intro
  • SQL/Nerds Modeling (parts)
  • History Repeats Itself: Sensible and NonsenSQL Aspects of the NoSQL Hoopla, by C. Mohan, EDBT 2013

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

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/intro-db.pdf
• Lab: SQL
• Programming assignment: Using SQL for data analysis and cleaning (check NYU Classes)

Big Data Foundations and Infrastructure (3 weeks)

Week 5 - Feb 22: Introduction to Map Reduce

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-intro.pdf
• Lab: Hands-on Hadoop (local and AWS)
• Quiz 1 (Map Reduce) -- check http://www.newgradiance.com/services

Week 6 - Feb 29: MapReduce Algorithm Design Patterns

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-algo-design.pdf
• Lab: Hands-on Hadoop (HPC)
• Programming assignment: Map Reduce (check NYU Classes)
• Readings:
  • 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)

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

• Lecture notes: ** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/paralleldb-vs-hadoop.pdf
• Lab: NoSQL
• Programming assignment: Pig and Spark
• Readings:
  • 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

• Additional 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

Transparency and Reproducibility (1 week)

Week 9 - March 21: Data Exploration and Reproducibility

• Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/data-science-reproducibility.pdf
• Lab: Hands-on reproducibility.
• Programming assignment: Exploring urban data (see NYU Classes)

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

Week 10 - March 28th: Finding similar items

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/similarity.pdf

• Reading: Chapter 3 Mining of Massive Datasets

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

Week 11 - April 4th: Association Rules

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2016/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 12 - April 11th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CDS)

• Lecture notes:
  • http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/SpatialQP.pdf

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

Week 14 - April 25th: 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