DS-GA 1004/CSCI-GA 2568 Big Data - Spring 2014

Instructor: Juliana Freire (firstname.lastname AT cs DOT nyu DOT edu)

Lecture time and location: Mondays, 7:10pm-9:00pm at Silver, room 207
Lab: Thursdays, 7:10pm-8:00pm at CIWW, room 109
Office hours time and location: Tuesday 4:00pm-5:00pm @ location 715 Broadway, room 1005

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 study the state of the art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing. In addition, we will examine real applications that require massive data analysis and how they can be implemented on Big Data platforms. 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 platforms, including Amazon AWS. Besides lectures given by the instructor, we will also have guest lectures by experts in some of the topics we will cover.

Syllabus and Estimated Times

The course consists of three main modules where we will tentatively cover the following topics: The schedule for classes, lecture notes, and required reading will be available at http://www.vistrails.org/index.php/Course:_Big_Data_Analysis

Principal Texts

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

Workload and Requirements

The workload will consist of online quizzes, using the Gradiance system and programming assignments.

For programming assignments, the instructor and graders will run your code and your grade will depend on the correctness of the outputs. Therefore, you must strictly follow the guidelines given for the programming assignments to ensure we will be able to run them.

Programming assignments must be done individually. Students must design and program their own solutions -- copying from other students or any other source is not acceptable.

Students are required to follow the following rules about academic honesty: http://www.cs.nyu.edu/web/Academic/Graduate/academic_dishonesty.html

Lateness policy: Late quizzes and assignments will not be accepted without a note from your physician or from your employer.


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 A3A872C4 . Make sure to use your official NYU email and id when you register.

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 instructor, send email to bigdata.new AT gmail DOT 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 Jan 24 13:20:11 EST 2014