Course: Big Data 2016
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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 Silver 207
- 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:
- Lab: getting started with MySQL
- Required Reading:
- Chapter 1 of Mining of Massive Data Analysis
- Suggested Reading:
Week 3 - Feb 8: Introduction to Databases, Relational Model and SQL (cont.)
- Lecture notes:
- Lab: SQL
- Programming assignment: Using SQL for data analysis and cleaning (check NYU Classes)
Week 4 - Feb 15: Holiday
Transparency and Reproducibility (1 week)
Week 5 - Feb 22: Data Exploration and Reproducibility
- Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/reproducibility-provenance.pdf
- Lab: Hands-on git and github (see NYU Classes). You will need to submit your work for this lab!
Big Data Foundations and Infrastructure (3 weeks)
Week 6 - Feb 29: 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 (juliana_freire: Ch. 2: Map-Reduce)
- Quiz is due on 2016-03-14 12:00 PM EST
Week 7 - March 7: MapReduce Algorithm Design Patterns
- Lecture notes:
- 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 8-- March 14th: Spring Break
Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK
- Lecture notes:
- Lab: Hands-on Pig
- Assignment: Hands-on Map-Reduce (see NYU Classes)
- 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
- Hive - A Warehousing Solution Over a Map-Reduce Framework. http://dl.acm.org/citation.cfm?id=1687609; http://www.vldb.org/pvldb/2/vldb09-938.pdf
- Pig Latin: A Not-So-Foreign Language for Data Processing. http://dl.acm.org/citation.cfm?id=1376726; http://infolab.stanford.edu/~olston/publications/sigmod08.pdf
- Additional Suggested reading:
- 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
Big Data Algorithms, Mining Techniques, and Visualization (6 weeks)
Week 10 - March 28th: Finding similar items & Spark
- Reading:
- Spark: Cluster Computing with Working Sets by Zaharia et al. https://amplab.cs.berkeley.edu/wp-content/uploads/2015/03/SparkSQLSigmod2015.pdf
- Chapter 3 Mining of Massive Datasets
- On the resemblance and containment of documents by Andrei Broder. http://www.misserpirat.dk/main/docs/00000004.pdf
- Homework Assignment
- See quizzes on Gradiance -- Distance measures and document similarity.
Week 11 - April 4th: Large-Scale Visualization -- -- Invited lecture by Professor Claudio Silva
Week 12 - April 11th: Exploring Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CDS)
- Lab: Using Amazon AWS to analyze and visualize taxi data
Week 13 - April 18th: Data Cleaning - Invited lecture by Dr. Divesh Srivastava, AT&T Research
Week 14 - April 25th: Association Rules
- 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 15 - May 2: Graph Analysis
- Required Reading: Data-Intensive Text Processing with MapReduce. Chapters 5 -- Graph Algorithms