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Course: Big Data 2016

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= DS-GA 1004- Big Data: Tentative Schedule -- ''subject to change'' =
= DS-GA 1004- Big Data: Tentative Schedule -- ''subject to change'' =
 +
 +
[[Course: Big Data 2017]]
* Course Web page: http://vgc.poly.edu/~juliana/courses/BigData2016
* Course Web page: http://vgc.poly.edu/~juliana/courses/BigData2016
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** Kevin Ye
** Kevin Ye
-
* Lecture: Mondays, 4:55pm-7:35pm at 19 University Pl., room 102.
+
* Lecture: Mondays, 4:55pm-7:35pm at Silver 207
* Some classes will include a lab session, please  always ''bring your laptop''.
* Some classes will include a lab session, please  always ''bring your laptop''.
Line 18: Line 20:
= News =
= 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: 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: Access you NYU HPC account, which you will use for in-class exercises and homework assignments. See  [[NYU HPC Access Instructions]]
+
* 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; Lab: Computing infrastructure for the course ==
+
== Week 1 - Jan 25:  Course Overview ==
-
* Lecture notes:  http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/course-overview.pdf
+
* '''Lecture notes:''' http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/course-overview.pdf
-
* Reading: Chapter 1 of Mining of Massive Data Sets (version 1.1)
+
*''' Lab:'''  Computing infrastructure for the course
-
* Course survey: https://docs.google.com/forms/d/1LTiJwkDVvp0cF62Fw_d9Y86US5LCkorRUIQtV2T8KWE/viewform?usp=send_form
+
* '''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, Relational Model and SQL==
+
== Week 2 - Feb 1:  The evolution of Data Management and introduction to Big Data; Introduction to Databases and Relational Model ==
-
* In-class assignment: relational algebra
+
* '''Lecture notes:'''
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/data-management-evolution.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/intro-to-db.pdf
 +
* '''Lab:''' getting started with MySQL
 +
* '''Required Reading:'''
 +
** Chapter 1 of Mining of Massive Data Analysis
 +
* '''Suggested Reading:'''
 +
** [http://philip.greenspun.com/sql/introduction.html Greenspun's SQL for Web Nerds Intro]
 +
** [http://philip.greenspun.com/sql/data-modeling.html SQL/Nerds Modeling (parts)]
 +
** [https://docs.google.com/file/d/0B7lNUaak0bK1NDBWZU5XTmItdGc/edit 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.) ==
== Week 3 - Feb 8: Introduction to Databases, Relational Model and SQL (cont.) ==
-
* Lab: SQL
+
*''' Lecture notes:'''
-
* Programming assignment: Using SQL for data analysis and cleaning  
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/relational-algebra.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/sql.pdf
 +
* '''Lab:''' SQL  
 +
* '''Programming assignment:''' Using SQL for data analysis and cleaning (check NYU Classes)
== Week 4 - Feb 15: Holiday ==
== Week 4 - Feb 15: Holiday ==
-
= Big Data Foundations and Infrastructure (3 weeks) =
+
= Transparency and Reproducibility  (1 week) =
-
== Week 5 - Feb 22:  Introduction to Map Reduce ==
+
== Week 5 - Feb 22: Data Exploration and Reproducibility ==
-
* Quiz 1 (Map Reduce) -- check http://www.newgradiance.com/services
+
* '''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!
-
* Lab: Hands-on Hadoop (local and AWS)
+
= Big Data Foundations and Infrastructure (3 weeks) =
-
== Week 6 - Feb 29: MapReduce Algorithm Design Patterns ==
+
== Week 6 - Feb 29:  Introduction to Map Reduce ==
-
* Lab: Hands-on Hadoop (HPC)
+
*''' Lecture notes:''' http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-intro.pdf
-
* Programming assignment: Map Reduce (check NYU Classes)
+
* '''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: Parallel Databases vs MapReduce; Introduction to SPARK==  
+
== Week 7 - March 7: MapReduce Algorithm Design Patterns  ==
-
* Lab: Hands-on SPARK (HPC)
+
*''' Lecture notes:'''
-
* Programming assignment: check NYU Classes on March 10th
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-recap.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-algo-design-patterns.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/mapreduce-algo-design-relations.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 8 -- March 14th: Spring Break ==
+
== Week 8-- March 14th: Spring Break ==
-
= Transparency and Reproducibility  (1 week) =
+
== Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK==  
-
== Week 9 - March 21: Data Exploration and Reproducibility  ==
+
*''' Lecture notes:'''
-
 
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/paralleldb-vs-hadoop.pdf
-
* Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/data-science-reproducibility.pdf
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/hive-pig.pdf
-
 
+
* '''Lab:''' Hands-on Pig
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* Lab: Hands-on reproducibility. Before class, please
+
* Assignment: Hands-on Map-Reduce (see NYU Classes)
-
** Download VisTrails 2.1.5 from here: http://www.vistrails.org/index.php/Downloads
+
* '''Readings''':
-
** Download the mta-analysis example: http://bigdata.poly.edu/~fchirigati/mda-class/mta-analysis.vt
+
** Benchmark DBMS vs MapReduce (2009): http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf
-
** Download the links for the input data: http://bigdata.poly.edu/~fchirigati/mda-class/mta-links.txt
+
** MapReduce: A Flexible Data Processing Tool: http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
-
** http://bigdata.poly.edu/~fchirigati/mda-class/hands-on.pdf
+
** 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
-
** Questions? Email Fernando at fchirigati@nyu.edu
+
** 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:'''
-
* Programming assignment 4: Exploring urban data (see NYU Classes)
+
** 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) =
= Big Data Algorithms, Mining Techniques, and Visualization (6 weeks) =
-
== Week 10 - March 28th:  Finding similar items ==
+
== Week 10 - March 28th:  Finding similar items & Spark ==
* Lecture notes:
* Lecture notes:
-
** http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/similarity.pdf
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/similarity.pdf
-
* Reading: Chapter 3 [http://vgc.poly.edu/~juliana/courses/BigData2015/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]  
+
* 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 [http://vgc.poly.edu/~juliana/courses/BigData2016/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]  
 +
** On the resemblance and containment of documents by Andrei Broder. http://www.misserpirat.dk/main/docs/00000004.pdf
* Homework Assignment
* Homework Assignment
-
** See quizzes on [http://www.newgradiance.com Gradiance] -- Distance measures and document similarity.  
+
** See quizzes on [http://www.newgradiance.com Gradiance] -- Distance measures and document similarity.
-
== Week 11 - April 4th: Association Rules ==
+
== Week 11 - April 4th: Large-Scale Visualization -- Invited lecture by Professor Claudio Silva ==
* Lecture notes:
* Lecture notes:
-
** http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/association-rules.pdf
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/intro-to-visualization.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/Plotting1.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/Plotting2.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/PlottingNotes.pdf
 +
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/Tufte.pdf
-
* Reading: Chapter 6 [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]
+
* Videos:
 +
**http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/movies/biopathways.mov
 +
**http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/movies/VisTrailsForParaView_Small.mov
 +
**http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/movies/defog-1150.mov
 +
**http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/visualization/movies/SevereTstorm.mov
-
* Suggested additional reading:  
+
== Week 12 - April 11th: Visualization: Using D3 -- Invited lecture by Bowen Yu ==
-
**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
+
* Lecture notes and lab:
-
** See quizes on [http://www.newgradiance.com Gradiance] -- Distance measures and document similarity.
+
** UPDATED: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/vis-d3_v2.1.pdf
-
== Week 12 - April 11th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CUSP) ==
+
== Week 13 - April 18th: Data quality: the other face of big data - Invited lecture by Dr. Divesh Srivastava, AT&T Research ==
-
* Lecture notes:
+
* Abstract: In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth “V” of big data is increasingly being recognized. In this talk, we highlight the substantial challenges that the first three “V”s, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner.  With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data.  This talk presents recent results that are relevant to big data quality management, focusing on the two major dimensions of        (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency.
-
** http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/SpatialQP.pdf
+
 
 +
* Bio: Divesh Srivastava is the head of Database Research at AT&T Labs-Research.  He is a Fellow of the Association for Computing Machinery (ACM) and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India. His research interests and publications span a variety of topics in data management.
-
* Lab: Using Amazon AWS to analyze and visualize taxi data
+
* Lecture notes: http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/bdq.pdf
-
** https://github.com/ViDA-NYU/aws_taxi
+
-
== Week 13 - April 18th: Parallel Databases ==
+
== Week 14 - April 25th: Exploring Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CDS) ==
* Lecture notes:
* Lecture notes:
-
** http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/paralleldb-vs-hadoop-2015.pdf
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/SpatialQP-2016.pdf
-
* Required reading:
+
* Lab: see NYU Classes
-
** 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 14 - April 25th: Graph Analysis ==
+
 
 +
 
 +
== Week 15 - May 2: Association Rules  ==
* Lecture notes:
* Lecture notes:
-
** http://vgc.poly.edu/~juliana/courses/BigData2015/Lectures/graph-algos.pdf
+
** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/association-rules.pdf
 +
 
 +
 
 +
* Reading: Chapter 6 [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf 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 quiz on [http://www.newgradiance.com Gradiance] -- Association Rules.
-
* 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 16 - May 9: Final Exam ==
== Week 17 - May 16: Project Presentations ==
== Week 17 - May 16: Project Presentations ==

Current revision as of 21:23, 29 January 2017

Contents

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

Course: Big Data 2017

  • 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

Week 1 - Jan 25: Course Overview

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

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

Week 4 - Feb 15: Holiday

Transparency and Reproducibility (1 week)

Week 5 - Feb 22: Data Exploration and Reproducibility

Big Data Foundations and Infrastructure (3 weeks)

Week 6 - Feb 29: Introduction to Map Reduce

Week 7 - March 7: MapReduce Algorithm Design Patterns

Week 8-- March 14th: Spring Break

Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK

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

Week 10 - March 28th: Finding similar items & Spark

  • 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: Visualization: Using D3 -- Invited lecture by Bowen Yu

Week 13 - April 18th: Data quality: the other face of big data - Invited lecture by Dr. Divesh Srivastava, AT&T Research

  • Abstract: In our Big Data era, data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Recent studies have shown that poor quality data is prevalent in large databases and on the Web. Since poor quality data can have serious consequences on the results of data analyses, the importance of veracity, the fourth “V” of big data is increasingly being recognized. In this talk, we highlight the substantial challenges that the first three “V”s, volume, velocity and variety, bring to dealing with veracity in big data. Due to the sheer volume and velocity of data, one needs to understand and (possibly) repair erroneous data in a scalable and timely manner. With the variety of data, often from a diversity of sources, data quality rules cannot be specified a priori; one needs to let the “data to speak for itself” in order to discover the semantics of the data. This talk presents recent results that are relevant to big data quality management, focusing on the two major dimensions of (i) discovering quality issues from the data itself, and (ii) trading-off accuracy vs efficiency.
  • Bio: Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is a Fellow of the Association for Computing Machinery (ACM) and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India. His research interests and publications span a variety of topics in data management.

Week 14 - April 25th: Exploring Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CDS)

  • Lab: see NYU Classes



Week 15 - May 2: 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 quiz on Gradiance -- Association Rules.


Week 16 - May 9: Final Exam

Week 17 - May 16: Project Presentations

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