Difference between revisions of "Course: Big Data 2016"
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** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/SpatialQP-2016.pdf | ** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/SpatialQP-2016.pdf | ||
* Lab: see NYU Classes | |||
Revision as of 19:41, 25 April 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 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
- Lecture notes:
- 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
- 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
Week 12 - April 11th: Visualization: Using D3 -- Invited lecture by Bowen Yu
- Lecture notes and lab:
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
- 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 quiz on Gradiance -- Association Rules.