The Data Analytics Track consists of a five course sequence that gives students a broad background in data science methods, applications and tools.  The track advisors are Deniz Yuret, Mehmet Gönen, and Barış Akgün. The students take one core course and four electives from two out of the three pools defined below.

Core courses
Comp421 Introduction to Machine Learning

Methods pool
Comp541 Deep Learning
Qmbu420 Big Data for Business and Public Sector
Comp341 Introduction to Artificial Intelligence
Comp470 Bioinformatics and Algorithms in Computational Biology
Elec430  Detection and Estimation Theory
Econ311  Introduction to Econometrics
Econ314  Econometric Methods for Time-Series and Forecasting
Econ511  Econometrics I
Math312  Mathematical Statistics

Applications pool
Comp437  Intelligent User Interfaces
Elec431  Adaptive Signal Processing
Econ512  Econometrics II
Econ513  Advanced Econometrics
Comp434 Computer and Network Security
Comp446 Algorithm Design and Analysis
Indr430 Decision Analysis
Comp408 Computer Vision and Pattern Recognition

Tools pool
Comp306 Database Management Systems
Indr564  Dynamic Programming
Indr501  Optimization Models and Algorithms
Comp415 Distributed Computing Systems