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Course: Big Data 2016

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(DS-GA 1004- Big Data: Tentative Schedule -- subject to change)
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* Course Web page: http://vgc.poly.edu/~juliana/courses/BigData2016
* Course Web page: http://vgc.poly.edu/~juliana/courses/BigData2016
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* Instructor: Professor Juliana Freire (http://vgc.poly.edu/~juliana)
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* Instructors:  
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** Professor Juliana Freire (http://vgc.poly.edu/~juliana)
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** Dr. Erin C Carson
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** Dr. Nicholas Knight
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* TAs:
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** Yuan Feng
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** Kevin Ye
* Lecture: Mondays, 4:55pm-7:35pm at 19 University Pl., room 102.  
* Lecture: Mondays, 4:55pm-7:35pm at 19 University Pl., room 102.  
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* Some classes will include a lab session, please  always ''bring your laptop''.
* Some classes will include a lab session, please  always ''bring your laptop''.

Revision as of 12:21, 23 January 2016

Contents

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

  • 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

Week 1 - Jan 25: Course Overview; The evolution of Data Management and introduction to Big Data

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

Week 3 - Feb 8: Introduction to Databases, Relational Model and SQL

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

Week 4 - Feb 15: Holiday

Big Data Foundations and Infrastructure (3 weeks)

Week 5 - Feb 22: Introduction to Map Reduce


Week 6 - Feb 29: Algorithm Design for MapReduce: Relational Operations

  • 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 7 - March 7: MapReduce Algorithm Design Patterns; Parallel Databases vs MapReduce


  • Programming assignment: check NYU Classes on March 10th

Week 8 -- March 14th: Spring Break

Transparency and Reproducibility (1 week)

Week 9 - March 21: Data Exploration and Reproducibility

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

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

Week 10 - March 28th: Finding similar items

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

Week 11 - April 4th: 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 quizes on Gradiance -- Distance measures and document similarity.

Week 12 - April 11th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CUSP)

Week 13 - April 18th: Parallel Databases

Week 14 - April 25th: Graph Analysis

  • 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 17 - May 16: Project Presentations

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