Concentration in Data Analytics

Program Overview

Concentration in Data Analytics

Data science is a broad, interdisciplinary field, and data scientists may have particular expertise in statistics, in programming, or in understanding of problems and data structures in particular areas of study. Students concentrating in Data Science at the Wilkes Honors College should manifest proficiency in all three areas, together with fluency or leadership in at least one of these.

Data Analytics is part of Data Science. In the Data Analytics concentration, students will be expected to attain fluency in computational skills, and proficiency in both statistical knowledge and domain expertise. This track was developed in collaboration with faculty from the College of Engineering and Computer Science (COECS).

Advisory Board:

Dr. Andia Chaves-Fonnegra (achavesfonnegra)
Dr. Yaouen Fily ( 
Dr. Terje Hoim (
Dr. Kevin Lanning (
Dr. Warren McGovern (
Professor Annina Ruest (

Courses indicated with a * are taken in the COECS and are typically available online.

I. Data literacy and quantitative reasoning (6 credits)

Course Title Prerequisites Credit
STA 2023 Honors Introduction to Statistics   3
COP 3073 Honors Introduction to Data Science STA 2023 3

Mathematical foundations (7 credits)

MAC 2311 Honors Calculus with Analytic Geometry I MAC 1147/ placement 4
MAD 2104 Honors Discrete Mathematics MAC 1105/ permission 3

MAC 2312 Honors Calculus w/ Analytic Geometry II (Prerequisite MAC 2311): 4 credits

II. Foundations of computer programming (9-10 credits)

Course Title Prerequisites Credit

One of the following:

COP 2000 Honors Foundations of Computer Programming   3
COP 2220 Introduction to Programming in C*   3
IDS 3932 Honors Beginner’s Programming for Biologists   3
ART 3657C Honors Introduction to Programming for Visual Arts   4

Both of the following:

COP 3014 Foundations of Computer Science* COP 2220, COP 2000, IDS 3932—Programming , OR Art 3657C 3
COP 3530 Data Structures and Algorithm Analysis*  COP 3014 and MAD 2104 3

III. Data proficiency (9 credits)

Course Title Prerequisites Credits
COP 3540 Introduction to Database Structures* COP 3530 3
CEN 4400  Introduction to Computer Systems Performance Evaluation* COP 3014 3
CAP 4770 Introduction to Data Mining and Machine Intelligence* COP 3530 3

STA 4821—Stochastic Models for CS (Prerequisite: MAC 2312 or MAC 2282): 3 credits.
COP 4703—Applied Database Systems (Prerequisite, COP 3540): 3 credits

Additional classes in intelligent systems (6 credits)

Course Title Prerequisites Credits

Two of the following

CAP 4613 Introduction to Deep Learning*   3
CAP 4630 Introduction to Artificial Intelligence* COP 3530 or OSM 4234 3
CAP 5615  Introduction to Neural Networks* COP 3530 3

Honors thesis (IDS 4970, taken twice for a total of 6 credits)


Total credits: 43-44 credits