CS9223 - Massive Data Analysis - Fall 2013

Instructors: Juliana Freire (juliana AT poly DOT edu) and Jerome Simeon (simeon AT us DOT ibm DOT com)

Class time and location: Monday 12:00pm-2:30pm @ 2MTC 9.007
Office hours time and location: Tuesday 1:00pm-2:00pm @ 2MTC room 10.097
Our class wiki is: http://www.vistrails.org/index.php/Course:_Big_Data_Analysis.

Course overview

Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. In this course, we will review the state of the art in massive data analysis. In addition to covering the specifics of different platforms, models, and languages, we will also look at real applications that perform massive data analysis and how they can be implemented on Big Data platforms. Topics we will discuss include: Map reduce/Hadoop, NoSQL stores, languages such as Pig Latin and JAQL, large-scale data mining and visualization. The course will consist of lectures based both on textbook material and scientific papers. It will also include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data tools and platforms.
Besides lectures given by the instructors, we will also have guest lectures by experts in statistics, information retrieval and visualization.

The readings for this course will consist of research papers and two recent books that are freely-available for download on the Web:


A course in database systems, covering application programming in SQL and other database-related languages such as XQuery; a course on algorithms and data structures; good programming skills.

Tentative topics

The topics we will cover include: A preliminary schedule for the classes and required reading is available at http://www.vistrails.org/index.php/Course:_Big_Data_Analysis


Assignments handed in on or before the due time will be graded for full credit. No late assignment will be accepted. Programming assignments must follow the guidelines given and they will be graded based on their outputs.


The grade for the course will be based on:

Gradiance Quizzes

You will need to access Gradiance for your quizzes at http://www.newgradiance.com/services. Here's a link to a guide on how to use Gradiance: http://www.gradiance.com/pub/stud-guide.html. Register and use the class token 00B06796 The quizzes appear to be sets of mutiple-choice questions. But you should think of the questions as if you were asked to work an ordinary, "long-answer" question. Work that question and keep the answer handy on a piece of paper. The multiple-choice question will typically sample your knowledge of the correct answer. You can try the work as many times as you like, and we hope everyone will eventually get 100%. Also notice that you have to wait 10 minutes between openings, so brute-force random guessing will not work.


If you need to reach the instructors, send email to nyupoly.cs9223@gmail.com


We thank Amazon for the AWS in Education Coursework grant that allowed the students to use their cloud infrastructure.
Juliana Freire
Last modified: Fri Oct 25 16:34:28 EDT 2013