Big Data Analytics and Systems

  • Genomic data analysis
  • Data mining
  • Machine learning
  • Data management

Big Data usually includes data sets with sizes beyond the abilities of common software tools to capture, manage, and process within a tolerable elapsed time. Big Data study can include fundamental techniques, theories, methodologies, and technologies for broad applications. These techniques, methodologies, and technologies can be computational, statistical, or mathematical in nature. The study of Big Data is an evolving topic, and one that overlaps with work in communications. One example is computational genomics, at the crux of computer engineering and genomics. The field’s goal is to store, organize, and analyze a large amount of genomic information. Another example is data mining, which involves the computational process of discovering patterns in large data sets. Its goal is to extract information from a data set and transform it into an understandable structure for further use.

To specialize in Big Data analytics and systems, you’ll want to take:
ECE 398BD, Making Sense of Big Data

And to work in computation genomics, you’ll also want to take:

CHBE 571/MCB 571/STAT 530 Bioinformatics
ANSC 545/CPSC 545/IB 507 Statistical Genomics

To specialize in data mining, you might consider taking:

ECE 594 Computational Models of Language
CS 512 Data Mining Principles
STAT 542 Statistical Learning
CS 466 Introduction to Bioinformatics
MCB 432 Computing in Molecular Biology
ANSC 448 Math Modeling in Life Sciences
BIOE 417 Computational Neurobiological Methods
CHEM 470 Computational Chemical Biology
CPSC 499 Bioinformatics and Genome Biology
CPSC 499 PERL & UNIX for Bioinformatics
IB 467 Principles of Systematics

Core Faculty in this area