{"id":226,"date":"2022-06-21T14:23:27","date_gmt":"2022-06-21T18:23:27","guid":{"rendered":"https:\/\/www.citadel.edu\/mathsci\/?page_id=226"},"modified":"2025-08-20T10:02:51","modified_gmt":"2025-08-20T14:02:51","slug":"math_data_analytics","status":"publish","type":"page","link":"https:\/\/www.citadel.edu\/mathsci\/programs-of-study\/math_data_analytics\/","title":{"rendered":"Mathematics (Concentration in Data Analytics), B.S."},"content":{"rendered":"\n
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\"Professor<\/figure>\n<\/div>\n\n\n\n
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Earn your Mathematics (Concentration in Data Analytics) Degree<\/h2>\n\n\n\n

The concentration in Data Analytics prepares majors with additional skills in statistical modeling (linear or non-linear), processing massive data analytically, and decision-making from the data processing outcomes.  <\/p>\n<\/div>\n<\/div>\n\n\n\n

As the 21st century is a data and high-technology century, there is a strong demand for graduates with mathematical, statistical and data analytical skills in many STEM and business sections within the public and private sectors. <\/p>\n\n\n\n

Structure of the Major<\/h3>\n\n\n\n

The BS in Mathematics with the concentration in Data Analytics provides students with up-to-date methods, technologies and developments in the fields of data-driven mathematical and statistical modeling and critical decision-making.\u00a0The BS in Mathematics and the BS in Mathematics with a concentration in Data Analytics both require a total of 123 hours. The concentration program allows the students to take courses like: CSCI 320 (Database Design) and CSCI 350 (Data Mining) and four statistics and data analysis courses: \u00a0STAT 362 (Experimental Design), STAT 366 (Applied Statistics), STAT 451 (Statistical Learning for Data Analytics) and STAT 461 (Data Analysis).<\/p>\n\n\n\n

\"Examples<\/figure>\n\n\n\n

Course breakdown by year:
[Freshman] [Sophomore<\/a>] [Junior<\/a>] [Senior<\/a>]<\/p>\n\n\n\n

Freshman Year<\/h3>\n\n\n\n
Introduction to the Practice of Mathematics<\/td>MATH 121<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Analytic Geometry and Calculus I<\/td>MATH 131<\/a><\/a><\/td>4<\/td>(4,0)<\/td><\/tr>
Freshman Science, Biology, Chemistry or Physics**<\/td> <\/td>4<\/td>(4,0)<\/td><\/tr>
A Modern Language<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
1st Year Basic ROTC<\/td> <\/td> <\/td> <\/td><\/tr>
Required Physical Education<\/td>RPED 250<\/td>2<\/td>(2,0)<\/td><\/tr>
First Year Seminar<\/td>LDRS 101<\/td>1<\/td>(1,0)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Analytic Geometry and Calculus II<\/td>MATH 132<\/a><\/td>4<\/td>(4,0)<\/td><\/tr>
Freshman Seminar<\/td>FSEM 101<\/td>3<\/td>(3,0)<\/td><\/tr>
Linked Composition <\/td>FSWI 101<\/td>3 <\/td> (3,0)<\/td><\/tr>
A Modern Language<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
Introduction to Programming with Python<\/td>CSCI 205<\/td>4<\/td>(4,0)<\/td><\/tr>
Freshman Ethical Fitness Seminar<\/td> LDRS 111<\/td> 0<\/td> (1,0)<\/td><\/tr>
1st Year Basic ROTC<\/td> <\/td> <\/td> <\/td><\/tr>
Required Physical Education<\/td>RPED 251<\/td>2<\/td>(2,0)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Sophomore Year<\/h3>\n\n\n\n
Analytic Geometry and Calculus III<\/td>MATH 231<\/a><\/td>4<\/td>(4,0)<\/td><\/tr>
Introduction to Discrete Structures<\/td>MATH 206<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Strand English<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
2nd Year Basic ROTC<\/td> <\/td> <\/td> <\/td><\/tr>
Health Fitness<\/td>RPED 260<\/td>0<\/td>(0,1)<\/td><\/tr>
Sophomore Seminar in Principal Leadership<\/td>LDRS 201<\/td>1
0<\/td>		(1,0)
(0,1)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Applied Mathematics I<\/td>MATH 234<\/a><\/td>4<\/td>(4,0)<\/td><\/tr>
Linear Algebra<\/td>MATH 240<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Intro to Probability and Statistics<\/td>STAT 261<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Strand Science**<\/td> <\/td> 3<\/td> (3,0)<\/td><\/tr>
Sophomore Seminar Service Learning Lab<\/td>LDRS 211<\/td>1
0<\/td>		(1,0)
(0,1)<\/td><\/tr>
2nd Year Basic ROTC<\/td> <\/td> <\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

 Junior Year<\/h3>\n\n\n\n
Mathematical Models and Applications<\/td>MATH 47<\/a>0<\/a><\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Data Mining<\/td>CSCI 350<\/td>3<\/td>(3,0)<\/td><\/tr>
Applied Statistics<\/td>STAT 366<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Professional Communication<\/td>COMM 260 <\/td>3<\/td>(3,0)<\/td><\/tr>
Leadership in Organizations<\/td> LDRS 371<\/td>3<\/td>(3,0)<\/td><\/tr>
Junior Ethics Enrichment Experience<\/td> LDRS 311<\/td>3<\/td>(3,0)<\/td><\/tr>
1st Year Advanced ROTC<\/td> <\/td> <\/td> <\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Experimental Design<\/td>STAT 362<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Database Design<\/td>CSCI 320<\/td>3<\/td>(3,0)<\/td><\/tr>
Strand Elective 3<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
Advanced ROTC<\/td> <\/td><\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Senior Year<\/h3>\n\n\n\n
Senior Seminar in Mathematics (Capstone)<\/td>MATH 495<\/a><\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Statistical Learning for Data Analytics<\/td>STAT 451<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Strand Elective 4<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
LDRS 411<\/td> <\/td> <\/td> <\/td><\/tr>
Advanced ROTC<\/td><\/td><\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Data Analysis<\/td>STAT 461<\/a><\/td>3<\/td>(3,0)<\/td><\/tr>
Strand Elective 5<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td><\/td>3<\/td>(3,0)<\/td><\/tr>
General Elective<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr>
Advanced ROTC<\/td> <\/td>3<\/td>(3,0)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

*A student must complete COMM 260 and one of ENGL 201, 202, 215, 218, or 219.<\/p>\n\n\n\n

**Science courses must be selected from Biology (101\/111 and 102\/112), Chemistry (151\/161 and 152\/162), or Physics (221\/271 and 222\/272)<\/p>\n\n\n\n

***Any mathematics or statistics course numbered at the 300 or 400 level.<\/p>\n\n\n\n

\n\n\n\n
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View the curriculum for your catalog year<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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View the Academic MAP for your catalog year<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Earn your Mathematics (Concentration in Data Analytics) Degree The concentration in Data Analytics prepares majors with additional skills in statistical modeling (linear or non-linear), processing massive data analytically, and decision-making from the data processing outcomes.   As the 21st century is a data and high-technology century, there is a strong demand for graduates with mathematical, statistical […]<\/p>\n","protected":false},"author":30,"featured_media":1093,"parent":38,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"page-program.php","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"class_list":["post-226","page","type-page","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nMathematics (Concentration in Data Analytics), B.S.<\/title>\n<meta name=\"description\" content=\"The Citadel's Mathematics (Concentration in Data Analytics) B.S. prepares 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