CS535 Big Data*

Level: Graduate students

Course Description
Storage, retrieval, analysis, and knowledge discovery using Big Data has made significant inroads in several domains in industry, research, and academia. In this course, we will look at the dominant software systems and algorithms for coping with the scale of Big Data. Topics covered include scalable computing models such as distributed machine learning and approximate algorithms for the real-time streaming data.  Also, we discuss large-scale non-traditional data storage frameworks including graph, key-value, and column-family storage systems. 

Semesters that this course was offered
Spring 2023, Spring 2022, Spring 2021, Spring 2020, Spring 2019, Fall 2017, Fall 2016, Fall 2015, Fall 2014, Fall 2013 (as CS581)

CS435 Introduction to Big Data*

Level: Senior undergraduate students or graduate students

Course Description
Modern scientific instruments and Internet-scale applications generate voluminous data pertaining to vital signs, weather phenomena, social networks that connect millions of users, and the origins of distant planets. Data produced in these settings hold the promise to significantly advanced knowledge. This course covers fundamental issues in Big Data. The course examines issues related to data organization, storage, retrieval, analysis and knowledge discovery at scale. This will include topics such as large-scale data analysis frameworks, data storage systems, self-descriptive data representations, and case studies. This course will involve hands-on programming assignments and term project using real-world datasets. 

Semesters that this course was offered
Fall 2023, Fall 2022, Fall 2021, Fall 2020, Fall 2019, Spring 2018, Spring 2017, Spring 2016, Spring 2015 (as CS480A)

CS481A5 Data Mining At Scale*

Level: Junior and senior undergraduate students

Course Description
This course introduces students to the foundational principles and practice of data mining for knowledge discovery with scalable strategies. This includes methods developed in the fields of statistics, machine learning, and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract insights and patterns. Topics include understanding characteristics of data, classification, preprocessing data, frequent item set analysis, clustering analysis, and scalable performance. We will use a software package and a distributed computing framework to understand algorithms and their use and limitations. The course will include hands-on laboratory sessions, with data mining case studies using real-world data such as networks, social networks, linguistics, ecology, geo-spatial applications, and psychology.

Semesters that this course was offered
Spring 2023

CS200 Algorithms and Data Structures

Level: Lower devision undergraduate students

Semesters that this course was offered
Spring 2013, Fall 2012, Spring 2012, Fall 2011

CS480 Principles of Data Management*

Level: Senior undergraduate students

Semesters that this course was offered
Spring 2013