Jeremy Kepner, Fellow & Head MIT Supercomputing Center
Enrollment: Limited: Advance sign-up required
Sign-up by 01/06
Limited to 30 participants
Attendance: Participants must attend all sessions
Prereq: Linear Algebra
"Big Data" describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields (e.g., internet search, healthcare, finance, social media, defense, ...) is increasing at a rate well beyond our ability to analyze the data. Many technologies (e.g., spreadsheets, databases, graphs, linear algebra, ...) have been developed to address these challenges. The common theme amongst these technologies is the need to store and operate on data as whole collections instead of as individual data elements. This class describes the common mathematical foundation of these data collections (associative arrays) that apply across a wide range of applications and technologies. Associative arrays unify and simplify Big Data leading to rapid solutions to Big Data volume, velocity, and variety problems. Understanding these mathematical foundations allows the student to see past the differences that lie on the surface of Big Data applications and technologies and leverage their core mathematical similarities to solve the hardest Big Data challenges.
Contact: Jeremy Kepner, 2nd Floor, 300 Tech Sq, 781 981-3108, KEPNER@LL.MIT.EDU
|Jan/10||Tue||11:00AM-01:00PM||2nd Flr 300 Tech Sq, Bring lunch if you like|
Preface and Chapter 1 of "Mathematics of Big Data" text
|Jan/17||Tue||11:00AM-01:00PM||2nd Flr 300 Tech Sq, Bring lunch if you like|
Chapter 2 and Chapter 3 of "Mathematics of Big Data" text. Introduction to D4M (http://d4m.mit.edu).
|Jan/24||Tue||11:00AM-01:00PM||2nd Flr 300 Tech Sq, Bring lunch if you like|
Chapters 4, 5, 6, 7 of "Mathematics of Big Data" text.
|Jan/31||Tue||11:00AM-01:00PM||2nd Flr 300 Tech Sq, Bring lunch if you like|
Chapters 8, 9 of "Mathematics of Big Data"