DS-GA 1004-001 - Spring 2016

Big Data

Juliana Freire

Center for Data Science


Lecture time: Monday, 4:55pm-7:35pm
Location: Silver Building, room 207
Some lectures include a lab session -- ''always bring your laptop.''

Instructors: Professor Juliana Freire, Dr. Erin Carson, Dr. Nick Knight
Office hours time and location: Mondays 2-3pm WWH 619; Tuesdays 2-3pm WWH 921

TAs and office hours
Class Wiki: http://www.vistrails.org/index.php/Course:_Big_Data_2016

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 is available at http://www.vistrails.org/index.php/Course:_Big_Data_2016

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, programming assignments, a project, and a final exam.

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, unless otherwise noted. 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, assignments, or project will not be accepted without a note from your physician or from your employer.

Evaluation

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 E41736B4. Make sure to use your official NYU email and id (the id consists of your initials and a number) 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.

Communication

To reach the instructor and TAs, use NYU Classes.

Acknowledgment

We thank Amazon for the AWS in Education Coursework grant which gives students taking this course access to their cloud infrastructure.
Juliana Freire
Last modified: Mon Apr 4 09:51:11 EDT 2016