Big Data Analytics


Big Data Analytics

MET CS 777 (4 credits)

This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent.

2022FALLMETCS777 A1, Sep 7th to Dec 7th 2022

Days Start End Type Bldg Room
W 06:00 PM 08:45 PM HAR 212

2022FALLMETCS777 O1, Sep 6th to Oct 24th 2022

Days Start End Type Bldg Room
ARR TBD TBD ROOM

2023SPRGMETCS777 A1, Jan 19th to Apr 27th 2023

Days Start End Type Bldg Room
R 06:00 PM 08:45 PM MET 101

2023SPRGMETCS777 O1, Jan 17th to Mar 6th 2023

Days Start End Type Bldg Room
ARR TBD TBD ROOM

 

Format & Syllabus