Bachelor of Science Data Science (BS)
Department website: https://www.odu.edu/datascience
Dr. Frank Liu, School Director (fliu@odu.edu)
The increased amount of available data has escalated the demand for data science professionals. The purpose of the BS in Data Science program is to provide students with foundational knowledge in the core competency areas of data science. Students will learn to use data to identify trends and patterns, solve problems, communicate results, and recommend solutions. The program will provide opportunities for students to practice these skills across application areas from different domains (e.g., geography, business, education). Graduates of this program will have the computer science, mathematics and statistics, and data analytics knowledge, skills, and abilities to work as data professionals.
For more information about the program contact Trent Buskirk, Undergraduate Program Director (tbuskirk@odu.edu).
Requirements
Lower-Division General Education
Code | Title | Credit Hours |
---|---|---|
Written Communication | 6 | |
Oral Communication | 3 | |
Mathematics | 3 | |
Language and Culture | 0-6 | |
Information Literacy and Research | 3 | |
Human Behavior | 3 | |
Human Creativity | 3 | |
Interpreting the Past | 3 | |
Literature | 3 | |
Philosophy and Ethics | 3 | |
The Nature of Science | 8 | |
Impact of Technology | 3 |
Mathematics: MATH 162M required.
Human Behavior: May not be met with DASC 205S or SOC 205S.
Philosophy and Ethics: Met with DASC 357E/PHIL 357E in the major.
Impact of Technology: Met with BDA 200T in the major.
Upper-Division General Education
Met in the major.
Requirements for Graduation
Requirements for graduation include the following:
- Minimum of 120 credit hours.
- Minimum of 30 credit hours overall and 12 credit hours of upper-level courses in the major program from Old Dominion University.
- Minimum overall cumulative grade point average of C (2.00) in all courses taken.
- Minimum overall cumulative grade point average of C (2.00) in all courses taken toward the major.
- Minimum overall cumulative grade point average of C (2.00) in all courses taken toward a minor.
- Completion of ENGL 110C, ENGL 211C or ENGL 231C, and the writing intensive (W) course in the major with a grade of C or better. The W course must be taken at Old Dominion University.
- Completion of Senior Assessment.
Data Science Major
Code | Title | Credit Hours |
---|---|---|
General Education | ||
Complete lower-division requirements | 35-41 | |
Upper Division General Education (met in the major) | ||
Foundation Courses | ||
CS 153 | Introduction to Programming with Python | 4 |
CS 251 | Programming with Java | 4 |
MATH 163 | Precalculus II | 3 |
STAT 130M | Elementary Statistics | 3 |
Core Requirements | ||
BDA 200T | Elements of Data Science | 3 |
DASC/SOC 205S | Data, Technology, Society | 3 |
DASC 300 | Foundations of Data Science | 3 |
DASC/PHIL 357E | Ethics and Data | 3 |
DASC 434 | Data Science Research Methods | 3 |
IT 360T | Principles of Information Technology | 3 |
IT 450 | Database Concepts | 3 |
STAT 310 | Introductory Data Analysis | 3 |
DASC 436W | Data Science Capstone Project * | 3 |
Complete an area of specialization (27-29 credits) | 27-29 | |
Total Credit Hours | 103-111 |
- *
Writing Intensive: C or better required.
No more than two classes, or six credits, may be counted for both the major and a minor. Some minors may allow fewer credits to share.
Data Science Areas of Specialization
Students in the Bachelor of Science in Data Science degree program must focus their studies in one of the specialized areas listed below.
The Artificial Intelligence and Machine Learning area requires completion of the following:
Code | Title | Credit Hours |
---|---|---|
Required Courses | ||
CS 252 | Introduction to Unix for Programmers | 1 |
CS 361 | Data Structures and Algorithms | 3 |
MATH 211 | Calculus I | 4 |
MATH 212 | Calculus II | 4 |
BDA 411 | Introduction to Machine Learning | 3 |
or CS 422 | Introduction to Machine Learning | |
CS 480 | Introduction to Artificial Intelligence | 3 |
or MSIM 480 | Introduction to Artificial Intelligence | |
Select three of the following approved area electives: | 9 | |
Object-Oriented Design and Programming | ||
Web Science | ||
Introduction to Game Development | ||
Applied Machine Learning in Cybersecurity | ||
Introduction to Machine Learning for Data Analytics Engineering | ||
Total Credit Hours | 27 |
The Data Visualization area requires completion of the following:
Code | Title | Credit Hours |
---|---|---|
Required Courses | ||
BNAL 206 | Business Analytics I | 3 |
BNAL 306 | Business Analytics II | 3 |
BNAL 403 | Data Visualization and Exploration | 3 |
CS 252 | Introduction to Unix for Programmers | 1 |
CS 361 | Data Structures and Algorithms | 3 |
ECE 406 | Computer Graphics and Visualization | 3 |
GAME 201T | Introduction to Game Studies | 3 |
MATH 211 | Calculus I | 4 |
Select two of the following approved area electives: | 6 | |
History of Graphic Design | ||
Social Science and Crime Mapping | ||
Introduction to Game Development | ||
Transportation Data Analytics | ||
Visual Design and Digital Graphics for Games | ||
Advanced Visual Design and Digital Graphics for Games | ||
Web Site and Web Page Design | ||
Total Credit Hours | 29 |
The Geospatial Information Systems area requires completion of the following:
Code | Title | Credit Hours |
---|---|---|
GEOG 102T | Digital Earth: Geospatial Technology and Society | 3 |
GEOG 402 | Geographic Information Systems | 3 |
GEOG 404 | Digital Techniques for Remote Sensing | 3 |
GEOG 419 | Spatial Analysis of Coastal Environments | 3 |
GEOG 425 | Internet Geographic Information Systems | 3 |
GEOG 432 | Advanced GIS | 3 |
GEOG 462 | Advanced Spatial Analysis | 3 |
GEOG 463 | GIS Programming | 3 |
GEOG 473 | Geographic Information Systems for Emergency Management | 3 |
Total Credit Hours | 27 |
Electives
Elective credit may be needed to meet the minimum of 120 hours required for the degree.
Degree Program Guide
The Degree Program Guide is a suggested curriculum to complete this degree program in four years. It is just one of several plans that will work and is presented only as broad guidance to students. Each student is strongly encouraged to develop a customized plan in consultation with their academic advisor. Additional information can also be found in Degree Works.
Specialization Area: Artificial Intelligence and Machine Learning
Freshman | ||
---|---|---|
Fall | Credit Hours | |
ENGL 110C | English Composition (C or better required) | 3 |
Oral Communication | 3 | |
Information Literacy and Research | 3 | |
Mathematics (MATH 162M required) | 3 | |
DASC/SOC 205S | Data, Technology, Society | 3 |
Credit Hours | 15 | |
Spring | ||
ENGL 211C or ENGL 231C |
Writing, Rhetoric, and Research (C or better required) or Writing, Rhetoric, and Research: Special Topics |
3 |
Interpreting the Past | 3 | |
Human Behavior (may not use DASC 205S or SOC 205S) | 3 | |
MATH 163 | Precalculus II | 3 |
BDA 200T | Elements of Data Science | 3 |
Credit Hours | 15 | |
Sophomore | ||
Fall | ||
Nature of Science I | 4 | |
STAT 130M | Elementary Statistics | 3 |
CS 153 | Introduction to Programming with Python | 4 |
CS 252 | Introduction to Unix for Programmers | 1 |
Language & Culture I (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
Nature of Science II | 4 | |
CS 251 | Programming with Java | 4 |
MATH 211 | Calculus I | 4 |
STAT 310 | Introductory Data Analysis | 3 |
Credit Hours | 15 | |
Junior | ||
Fall | ||
DASC 300 | Foundations of Data Science | 3 |
IT 360T | Principles of Information Technology | 3 |
CS 361 | Data Structures and Algorithms | 3 |
CS 480 or MSIM 480 |
Introduction to Artificial Intelligence or Introduction to Artificial Intelligence |
3 |
Language & Culture II (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
DASC/PHIL 357E | Ethics and Data | 3 |
IT 450 | Database Concepts | 3 |
MATH 212 | Calculus II | 4 |
BDA 411 or CS 422 |
Introduction to Machine Learning or Introduction to Machine Learning |
3 |
General Elective | 3 | |
Credit Hours | 16 | |
Senior | ||
Fall | ||
Literature | 3 | |
DASC 434 | Data Science Research Methods | 3 |
Approved Area Electives | 6 | |
General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
Human Creativity | 3 | |
DASC 436W | Data Science Capstone Project (C or better required) | 3 |
Approved Area Elective | 3 | |
General Electives | 5 | |
Credit Hours | 14 | |
Total Credit Hours | 120 |
Specialization Area: Data Visualization
Freshman | ||
---|---|---|
Fall | Credit Hours | |
ENGL 110C | English Composition (C or better required) | 3 |
Oral Communication | 3 | |
Information Literacy and Research | 3 | |
Mathematics (MATH 162M required) | 3 | |
DASC/SOC 205S | Data, Technology, Society | 3 |
Credit Hours | 15 | |
Spring | ||
ENGL 211C or ENGL 231C |
Writing, Rhetoric, and Research (C or better required) or Writing, Rhetoric, and Research: Special Topics |
3 |
Interpreting the Past | 3 | |
Human Behavior (may not use DASC 205S or SOC 205S) | 3 | |
MATH 163 | Precalculus II | 3 |
BDA 200T | Elements of Data Science | 3 |
Credit Hours | 15 | |
Sophomore | ||
Fall | ||
Nature of Science I | 4 | |
STAT 130M | Elementary Statistics | 3 |
CS 153 | Introduction to Programming with Python | 4 |
CS 252 | Introduction to Unix for Programmers | 1 |
Language & Culture I (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
Nature of Science II | 4 | |
CS 251 | Programming with Java | 4 |
MATH 211 | Calculus I | 4 |
STAT 310 | Introductory Data Analysis | 3 |
Credit Hours | 15 | |
Junior | ||
Fall | ||
DASC 300 | Foundations of Data Science | 3 |
IT 360T | Principles of Information Technology | 3 |
CS 361 | Data Structures and Algorithms | 3 |
BNAL 206 | Business Analytics I | 3 |
Language & Culture II (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
DASC/PHIL 357E | Ethics and Data | 3 |
IT 450 | Database Concepts | 3 |
GAME 201T | Introduction to Game Studies | 3 |
BNAL 306 | Business Analytics II | 3 |
General Elective | 3 | |
Credit Hours | 15 | |
Senior | ||
Fall | ||
Literature | 3 | |
DASC 434 | Data Science Research Methods | 3 |
BNAL 403 | Data Visualization and Exploration | 3 |
Approved Area Elective | 3 | |
General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
Human Creativity | 3 | |
DASC 436W | Data Science Capstone Project (C or better required) | 3 |
ECE 406 | Computer Graphics and Visualization | 3 |
Approved Area Elective | 3 | |
General Elective | 3 | |
Credit Hours | 15 | |
Total Credit Hours | 120 |
Specialization Area: Geospatial Information Systems
Freshman | ||
---|---|---|
Fall | Credit Hours | |
ENGL 110C | English Composition (C or better required) | 3 |
Oral Communication | 3 | |
Information Literacy and Research | 3 | |
Mathematics (MATH 162M required) | 3 | |
DASC/SOC 205S | Data, Technology, Society | 3 |
Credit Hours | 15 | |
Spring | ||
ENGL 211C or ENGL 231C |
Writing, Rhetoric, and Research (C or better required) or Writing, Rhetoric, and Research: Special Topics |
3 |
Interpreting the Past | 3 | |
Human Behavior (may not use DASC 205S or SOC 205S) | 3 | |
MATH 163 | Precalculus II | 3 |
BDA 200T | Elements of Data Science | 3 |
Credit Hours | 15 | |
Sophomore | ||
Fall | ||
Nature of Science I | 4 | |
CS 153 | Introduction to Programming with Python | 4 |
STAT 130M | Elementary Statistics | 3 |
GEOG 102T | Digital Earth: Geospatial Technology and Society | 3 |
Elective | 1 | |
Credit Hours | 15 | |
Spring | ||
Nature of Science II | 4 | |
CS 251 | Programming with Java | 4 |
STAT 310 | Introductory Data Analysis | 3 |
Elective(s) | 4 | |
Credit Hours | 15 | |
Junior | ||
Fall | ||
DASC 300 | Foundations of Data Science | 3 |
IT 360T | Principles of Information Technology | 3 |
GEOG 402 | Geographic Information Systems | 3 |
GEOG 404 | Digital Techniques for Remote Sensing | 3 |
Language & Culture I (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
DASC/PHIL 357E | Ethics and Data | 3 |
IT 450 | Database Concepts | 3 |
GEOG 419 | Spatial Analysis of Coastal Environments | 3 |
GEOG 425 | Internet Geographic Information Systems | 3 |
Language & Culture II (if needed) or General Elective | 3 | |
Credit Hours | 15 | |
Senior | ||
Fall | ||
Literature | 3 | |
DASC 434 | Data Science Research Methods | 3 |
GEOG 432 | Advanced GIS | 3 |
GEOG 462 | Advanced Spatial Analysis | 3 |
Elective | 3 | |
Credit Hours | 15 | |
Spring | ||
Human Creativity | 3 | |
DASC 436W | Data Science Capstone Project (C or better required) | 3 |
GEOG 463 | GIS Programming | 3 |
GEOG 473 | Geographic Information Systems for Emergency Management | 3 |
Elective | 3 | |
Credit Hours | 15 | |
Total Credit Hours | 120 |