B.S. in Data Science and Analytics

Area of Study Overview

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The Bachelor of Science in Data Science and Analytics prepares students to turn data into insight, strategy, and impact. Designed for working adults and offered fully online, the program builds a strong foundation in mathematics, statistics, and computer science while developing practical skills in data analysis, predictive modeling, machine learning, and data visualization.

Students learn to manage and analyze complex datasets, apply artificial intelligence and machine learning techniques to real-world problems, and communicate data-driven findings effectively to diverse audiences. The program also emphasizes ethical and responsible data practices, ensuring graduates are prepared to navigate the legal and societal implications of data science.

Graduates will be equipped with in-demand technical and analytical skills that support informed decision-making across industries and prepare them for careers in today’s data-driven economy.

 

Credit Distribution

I. General Education Featured Courses (46 Credits)

TESU Featured Courses are listed as a guide. Other courses may satisfy the areas and can be viewed on our website under General Education Courses.  To plan your program please contact an academic advisor.

A. Intellectual and Practical Skills (15 Credits)

ENC-1010	Writing for Success	3
ENC-1020	Writing for Success II	3
COM-2090	Public Speaking	3
MAT-1150	Intermediate Algebra	3
SOS-1100	Fact, Fiction, or Fake? Information Literacy Today	3

For all TESU course options that will satisfy this area of the degree, go to Intellectual and Practical Skills.

B. Civic and Global Leadership (9 Credits)

SOC-1010	Our Changing World: An Introduction to Sociology	3
ETH-2200	Leading the Way: A Path Towards Ethical Leadership	3
POS-1100	American Government	3

For additional TESU course options that will satisfy this area of the degree, go to Civic and Global Leadership.

C. Knowledge of Human Cultures (15 Credits)

CRJ-2800	Forensic Science	3
HIS-1130	American History I	3
FIL-1100	Exploring American Cinema	3

Select two featured courses from the list below:

SOC-2910	Criminology	3
HIS-1140	American History II	3
HUM-1030	Introduction to the Humanities III: Music	3

For additional TESU course options that will satisfy this area of the degree, go to Knowledge of Human Cultures.

D. Scientific Knowledge (7 Credits)

CIS-1070	Computer Concepts and Applications	3
-	Any Lab Science	4

For additional TESU course options that will satisfy this area of the degree, go to Scientific Knowledge.

II. Area of Study (42 Credits)

-	Any Math Course	3
-	Any Lab Science	4

A. Core Area of Study Courses (23 Credits)

STA-2010

Principles of Statistics

3

Predictive Analytics I- Machine Learning Tools*

Predictive Analytics II- Neural Nets and Regression*

Predictive Analytics III- Dimension Reduction, Clustering and Association Rules*

Optimization- Linear Programming*

Introduction to Network Analysis*

Forecasting Analytics

Interactive Data Visualization*

B. Area of Study Electives: Students select four of the following courses (12 Credits)

SQL- Introduction to Database Queries*

Regression Analysis*

Introduction to R Programming

Python for Analytics

Financial Risk Modeling*

Spatial Statistics with Geographic Information Systems*

Integer and Nonlinear Programming and Network Flow

Risk Simulation and Queuing*

R Programming Intermediate

*Upper-division course as designated by ACE.

III. Electives (32 Credits)

For TESU course options, go to Elective Courses.

TRANSFER REQUIREMENT: TESU accepts credits in transfer from accredited institutions as well as non-collegiate providers. View information and resources on where you may find additional learning experiences.

Total Credit Hours: 120

Learning Outcomes

Graduates of the Data Science and Analytics program will be able to:

• utilize key technologies in data science and analytics, including data mining, machine learning, visualization techniques, predictive modeling, and statistics; and
• apply knowledge of statistical data analysis and quantitative modeling techniques to solve real world problems.