Data Science (BS)

Data Science is a discipline that provides students with the necessary skills to understand the theory and algorithms utilized in data science and to be able to implement and apply them. It provides students with the essential background, understanding, knowledge, and tools for discovering knowledge from data. Data science is the confluence of many areas including machine learning, artificial intelligence, information storage and retrieval, statistics and operations research. The courses in the program cover the complete knowledge discovery process. Students learn skills such as sampling techniques, data and knowledge representations, cleansing and preprocessing data sets, and machine learning algorithms. They apply them to build models for both unsupervised and supervised tasks such as forecasting, classification and unsupervised learning tasks such as clustering. They understand and have the skill sets for evaluating and comparing models, building aggregate and hybrid models through techniques such as boosting and stacking. Big Data, Data Warehousing and OLAP are also covered as well as data and knowledge visualization.

The Data Science majors can take several elective courses, and in-depth study in a number of specializations such as in Engineering Analytics and Economics and Applied Analytics.

Data scientists are in demand and employed in a range of organizations and domains, from tech startups to large companies in healthcare, insurance, finance and banking, marketing and retail, food industry and agriculture, retail, entertainment and media, government agencies and energy production.

The general requirements for the data science major are:

Course List

Code	Title	Hours
Data Science
CS 101	Introduction to Programming	4.0
CS 102	Data Structures	3.0
CS 141	Introduction to Python Programming	3.0
CS 200	Advanced Programming Concepts and Languages	3.0
CS 210	Advanced Data Structures and Algorithms	3.0
CS 360	Fundamentals of Data Science	3.0
CS 370	Database Management Systems	3.0
CS 461	Artificial Intelligence	3.0
CS 462	Machine Learning	3.0
CS 463	Knowledge Discovery and Data Mining	3.0
CS 472	Distributed Databases and Big Data	3.0
CS 480	Social and Professional Issues in Computing	2.0
CS 494	Capstone Project for Data Science	3.0
Take three courses from the following list:		9.0
CIS 230	Introduction to Computer Forensics
CIS 330	Advanced Computer Forensics
CIS 415	Applied Cryptography
CIS 445	Integrative Programming and Technology
CS 390	Introduction to Software Engineering
CS 435
For specialization, take three courses in either Engineering Analytics or Applied Analytics.		9.0
For Engineering Analytics, take three courses from the following list:
IME 302	Introduction to Quality Engineering
IME 341	Introduction to Manufacturing Processes
IME 385	Introduction to Logistics and Supply Chain
IME 386	Industrial and Managerial Engineering
IME 412	Design and Analysis of Experiments
For Applied Analytics, take three courses from the following list:
ECO 100	Introduction to Economics
Q M 426	Business Forecasting
MIS 173	Introduction to Business Analytics
MIS 272	Business Analytics Tools and Techniques
MIS 471	Applied Machine Learning for Business
MIS 473	Data Visualization for Business Analytics
Q M 464	Decision Support Systems
or ECO 419	Introduction to Econometrics
Mathematics and Statistics
MTH 120	Discrete Mathematics	3.0
MTH 121	Calculus I	4.0
MTH 122	Calculus II	4.0
MTH 207	Elementary Linear Algebra With Applications	3.0
Select one of the following:		3.0-6.0
MTH 325	Probability and Statistics I
IME 311	Introduction to Engineering Statistical Methods
Q M 262 & Q M 263	Quantitative Analysis I and Quantitative Analysis II
Total Hours		74-77

A grade of C or better is required in all computer science and computer information systems courses presented in fulfillment of the major requirements.