Algorithms for Big Data

Chandra Chekuri

CS498ABG/ABU, Fall 2022

Course Summary

This course will describe some algorithmic techniques developed for handling large amounts of data that is often available in limited ways. Topics that will be covered include data stream algorithms, sampling and sketching techniques, and sparsification, with applications to signals, matrices, and graphs. Emphasis will be on the theoretical aspects of the design and analysis of such algorithms.

This version of the course is directed at senior level undergraduate students and beginning graduate students, and hence will not assume background in randomized algorithms. The pace of the course and the topics will be commensurately adjusted. The previous edition of this course will give a good idea of the topics to be covered although there will be some changes.

Prerequisites: CS 374 and CS 361, or comparable understanding and facility with algorithms and probability.


Instructor: Chandra Chekuri (3228 Siebel Center, chekuri at

Teaching assistant: Manuel Torres (manuelt2 at

Schedule, Lectures and Resources: See schedule page  

Office hours: Chandra (Fri 11am-noon, 3228 Siebel); Manuel (Wed 1-2pm, Siebel third floor lounge between offices 3304 & 3232 (south side of building)) 

Announcements: Ed; Enrollment is required (via this link). Almost all announcements will be made on Ed.

Homework/project submission: Gradescope (self-enrollment code: WVVPKD)

Grading policy: 40% Homework (4-5 biweekly), 2 x 20% Midterms, 20% final project

College of Engineering Syllabus Statements: See here for important information and regulations regarding academic integrity, disability accommodations, religious observances, and sexual misconduct reporting and here for information on FERPA rights.

Anti-racism and inclusivity: We will strive to make the course a welcoming and encouraging learning environment that is free of bias. Please bring any concerns on these issues to the attention of the instructor or report directly to Bias Assessment and Response Team (BART) ( Be kind, respectful, and professional to everyone and expect the same from others.
CS CARES and CS Values and Code of Conduct All members of the Illinois Computer Science department - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructor of this course is also available for issues related to this class.

Covid-19: We are in much better shape than a couple of years ago but Covid-19 continues to be disruptive in various ways. Please follow university policies on symptom vigilance, class attendance, and related academic concerns. The university has several resources for health concerns, both physical and mental. Please reach out and get help promptly as needed.
McKinley Health Center: 217-333-2700, 1109 South Lincoln Avenue, Urbana, Illinois 61801
Counseling Center: 217-333-3704, 610 East John Street, Champaign, IL 61820
Please do not hesitate to contact the instructor if you need help or assistance.



Project details will be announced later in the semester.