2024-25 Academic Catalog

Office of Academic Affairs

Office: 3501 Lee Hall
662-325-3742
P.O. Box BQ; Mississippi State, MS 39762

Center for Academic Excellence

Executive Director: Dr. Clay Armstrong

YMCA 1st Floor
Mailstop 9711 
Web site http://cae.msstate.edu
Telephone: (662) 325-2957
195 Lee Blvd.
Mississippi State, MS 39762

Mission

The Center for Academic Excellence works with all MSU students – especially incoming freshmen – to help assure their smooth transition to the university and success on their road to graduation. The Center promotes student learning and an enriched MSU student experience by providing services, programs, and resources that:

- assist the student with his or her transition into university life;

- aid the student's decision-making, especially during the freshman year; and

- help achieve personal and academic progress and growth, targeted toward graduation.
The Center's strategic goals are to:

- offer services, programs, and classes that assist the student's transition to MSU;

- support student academic planning and progress through high-quality academic advising and timely feedback;

- provide informative and engaging first-year classes and programs;

- provide academic support for all students;

- develop programs and take actions that are informed by analyses of relevant data; and

- engage the university in the support of students in their progress toward graduation.

The Center for Academic Excellence operates the College Ready program, a summer program through which an incoming freshman can take two college classes prior to their first fall semester at discounted cost. The primary goal of College Ready is to smooth the student’s transition to their new living and learning environment. The Center also includes the Freshman Year Navigator program, hiring 30 or more students each year to work as Navigators and help their assigned freshmen throughout their first year at MSU. 

The Center also provides Supplemental Instruction and tutoring in 80 or more challenging classes each semester. It also works closely with the University Academic Advising Center, which provides all advising for the freshman class’s largest major, Undeclared. Finally, the Center works with the Pathfinders program to emphasize the importance of class attendance – class attendance is the #1 predictor of student success.

University Academic Advising Center (UAAC)

Undeclared (UND)

Director: Lynda K. Moore
Professional Academic Coordinators: Bailey Berry, Wendy Dandass, Jermaine Jackson, Katy Richey, and Jaiki Shumpert

252 Famous Maroon Band Street.; Mail Stop 9729
Web site at http://www.uaac.msstate.edu/
Telephone (662) 325-4052; Fax (662) 325-4026
P.O. Box 6117, Mississippi State, MS 39762

UAAC Mission to Undeclared Students

The University Academic Advising Center was established to meet the needs of those students who have competing interests in more than one major area, as well as those who are uncertain of their career and educational goals. The professional staff at the center offer one-on-one advising services to traditional and non-traditional undergraduate students and provide accurate information concerning general curriculum requirements, university policies and procedures, campus resources and various programs of study. The center is committed to assisting students with the development of educational plans consistent with their life goals, objectives and abilities. Students normally remain Undeclared for no more than three semesters during which time advisors recommend courses that meet basic core requirements in relation to “majors of interest” for each individual student. Students must declare a major before completing 75 hours.

UAAC advisors traditionally recommend that Undeclared students enroll in 15-18 hours each fall and spring semester with careful considerations given to courses required in each student’s majors of interest. It is the goal of the center to assist each Undeclared student in enrolling in courses that satisfy the minimum core requirements for any major the student may later choose with respect to each department’s right to specify more stringent requirements than the University as a whole. However, ultimate responsibility for taking the UAAC staff’s advice rests with the student.

UAAC urges students to make appointments with advisors at the center to establish a plan of action. The University Academic Advising Center staff encourages all Undeclared majors to utilize services offered by the Career Center, the Counseling Center, the Learning Center, Center for Student Success, Student Support Services and other support programs offered by various units at MSU.

The UAAC advises for the University Studies degree, the Complete to Compete Program, and Applied Science.

 

University Studies - Complete to Compete Program (C2C)

Major C2C Coaches: Kristi Dearing, Kali Dunlap, Lynda Moore, and Jaiki Shumpert

The Bachelor of Science in University Studies is housed in the Office of the Provost and Executive Vice President (Academic Affairs).  A student who wishes to pursue the major in University Studies must enroll in the Complete to Compete (C2C) program and be eligible for admission to the degree program.  To be eligible, the student must meet the criteria of an adult learner, i.e. must be at least 21 years old, have earned at least 90 semester credit hours, have not earned a baccalaureate degree, and must not have attended a postsecondary institution within the last twenty-four months.

The University Studies degree is designed to provide eligible adult learners who have not completed a baccalaureate degree a pathway to return to Mississippi State University to complete a degree.  The curriculum is individualized to meet the needs and career goals of the returning adult learner. An eligible student will meet with a C2C Coach and work with faculty to develop the best degree plan possible.

Degree Requirements

English Composition
EN 1103English Composition I3
EN 1113English Composition II3
Humanities
See General Education Core6
Mathematics
See General Education Core3
Fine Arts
See General Education Core3
Natural Sciences
See General Education Core. Two labs required.6
Social Sciences
See General Education Core6
Oral Communication Requirement
CO 1003Fundamentals of Public Speaking3
or CO 1013 Introduction to Communication
Or other approved speech course
Computer Literacy
TECH 1273Computer Applications2-3
or BIS 1012 Introduction to Business Information Systems
Or other approved technology course
Extra University Core
See General Education Core or Advisor Approved Upper Level Courses24
Junior/Senior Writing Requirements
Consult with C2C Coach and/or Faculty Advisor3
Major Core Emphasis Areas
Consult C2C Coach and/or Faculty Advisor
Upper Level Courses (one, two, or three emphasis areas)30
General Electives25-28
Total Hours120

Bachelor of Science in Data Science 

The Bachelor of Science in Data Science is an interdisciplinary program that draws upon disciplines from multiple colleges. It is a 123-hour inter-college program designed to include three general areas of coursework: general education, program core, and applications of the data science fundamentals in specific body of knowledge such as geoinformatics, computational intelligence and cybersecurity, marketing, management information systems, statistical modeling, social science analytics, architectural design and built environment, and smart agriculture. The overall curriculum is designed to provide students with an ideal educational experience necessary to become effective professional data science experts. Under the proposed undergraduate curriculum, general education coursework will help data science students develop intellectual curiosity, critical thinking, and ethical and aesthetic awareness. The coursework for the core program will provide students with the opportunity to build a strong foundation in the key fields of data science that include computer science, mathematics and statistics, management information systems, communication, management/leadership, design, and ethics. The course sequences for several distinct areas of academic concentration will provide students with the opportunity to become data science experts in a specific area. 

English
EN 1103English Composition I3-4
or EN 1104 Expanded English Composition I
EN 1113English Composition II3
or EN 1173 Accelerated Composition II
Fine Arts
Any Gen Ed course3
Natural Sciences
2 lab based sciences required by Gen Ed6
Math
MA 1713Calculus I3
MA 1723Calculus II3
MA 2733Calculus III3
Humanities
PHI 1113Introduction to Logic3
Any additional Gen Ed course3
Social/Behavioral Sciences
DSCI 2013Data Science Literacy3
Any additional Gen Ed course3
Oral Communication
CO 3213Small Group Communication3
Technical Writing
CO 3223Communication & Media Research Methods3
Major Core
MA 3123Introduction to Statistical Inference3
MA 3113Introduction to Linear Algebra3
MA 4523Introduction to Probability3
or ST 4523 Introduction to Probability
CSE 1284Introduction to Computer Programming4
CSE 1384Intermediate Computer Programming4
CSE 2813Discrete Structures3
CSE 2383Data Structures and Analysis of Algorithms3
CSE 3763Ethical and Legal Issues in Computing3
CSE 4503Database Management Systems3
CSE 4633Artificial Intelligence3
BIS 3233Management Information Systems3
DSCI 2012Data Science Lab: Data Wrangling2
DSCI 2022Data Science Lab: Cloud, High-Performance, and Quantum Computing2
DSCI 3012Data Science Lab: Description, Analysis, and Inference2
DSCI 3013Fundamentals of Data Acquisition3
DSCI 3022Data Science Lab: Data Visualization2
DSCI 3032Data Science Lab: Artificial Intelligence2
DSCI 4013Data Visualization3
Concentration Courses 30
Total Hours123

Each area of concentration combines fundamental, field-specific content, concentration electives designed to apply data science to the field, and a six-hour practicum/capstone project. On their third year, students will have the opportunity to select a concentration area from the several available areas offered by the different college on campus.

Visualization and Visual Analytics for Built Environment Concentration

Fundamental Discipline Courses
Complete 8 of the following: 24
Design I
Introduction to Computing for ART
Intermediate Computing for Designers
Introduction of Multimedia I Design and Authoring
Virtual Design and Construction
Digital Design for Interiors
3/D CAD/Modeling
Environmental Building Systems I
Environmental Building Systems II
Architecture and Virtual Spaces
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23

Computational Agriculture and Natural Resources Concentration

Fundamental Discipline Courses
Choose 1 course from the following: 3
Introduction to Food and Resource Economics
Engineering Technology in Agriculture
Principles of Biochemistry
Plant Science
Animal Science
Choose 1 course from the following: 3
Introduction to Sustainable Bioproducts
Applied Ecology
Forest Ecology
Core Concentration Courses
Choose 6 hours from the following:6
CALS
Principles of Macroeconomics
Intermediate Microeconomics
Introduction to Sustainability Economics
Introductory Agribusiness Management
Introduction to Environmental Economics and Policy
Financial and Commodity Futures Marketing
Principles of Agricultural and Off-Road Machines
Precision Agriculture I
Precision Agriculture II
Essential Biochemical Concepts and Analysis
Protein Methods
Anatomy and Physiology
Introduction to Meat Science
CFR
Introduction to Bioproduct Industries
Materials and Processing of Structural Bioproducts
Fisheries Management
Landscape Ecology
Forest Measurements
Essentials of Biotechnology
Forest Resource Economics
Forest Ecology
Applied Courses
Choose 12 hours from the following: 12
CALS
Analysis of Food Markets and Prices
Applied Quantitative Analysis in Agricultural Economics
Economics of Precision Agriculture
Public Problems of Agriculture
Econometric Analysis in Agriculture Economics
Land Surveying
The Global Positional System and Geographic Information Systems in Agriculture and Engineering
Agricultural and Off-Road Machinery Management
Soil and Water Management
Introduction to Imaging in Biological Systems
Introduction to Remote Sensing Technologies
Integrative Protein Evolution
Introduction to Remote Sensing Technologies
Internet-Based Management in Livestock Industries
CFR
Wood Anatomy
Quantitative Methods in Sustainable Bioproducts
Wildlife & Fisheries Biometrics
Wildlife Techniques
Application of Spatial Technologies to Wildlife and Fisheries Management
Forest Description and Analysis
Forest Biometrics
Spatial Technologies in Natural Resources Management
Remote Sensing Applications
GIS for Natural Resource Management
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23

Business Information Systems Concentration

Fundamental Discipline Courses
Choose 2 courses from the following: 6
The Legal Environment of Business
Principles of Financial Accounting
Principles of Managerial Accounting
Principles of Macroeconomics
Principles of Microeconomics
Financial Management
Principles of Management
Principles of Marketing
International Logistics
Core Concentration Courses
BQA 4423Business Decision Analysis3
BIS 4533Decision Support Systems3
BIS 4113Business Information Systems Security Management3
BIS 4753Structured Systems Analysis and Design3
4000-level business course3
Non-business course from any of the data science concentrations3
Capstone
BIS 4763BIS Senior Seminar3
BQA 4413Business Forecasting and Predictive Analytics3

Marketing and Supply Chain Concentration

Fundamental Discipline Courses
BQA 4423Business Decision Analysis3
MKT 3013Principles of Marketing3
MKT 3323International Logistics3
Choose 1 course from the following: 3
The Legal Environment of Business
Principles of Financial Accounting
Principles of Managerial Accounting
Principles of Macroeconomics
Principles of Microeconomics
Financial Management
Principles of Management
Core Concentration Courses
Choose 3 courses from the following: 12
Decision Support Systems
Procurement
International Transportation
Internet Marketing
Physical Distribution Management
Marketing Research
Students will register for one non-business course for which they meet the prerequisites from any of the data science concentrations.
Capstone
Choose 2 from the following:
International Supply Chain Management
Business Forecasting and Predictive Analytics
Directed Individual Study in Business Quantitative Analysis

Social Data Analytics Concentration

Fundamental Discipline Courses
Choose 9 hours from the following (but no more than 6 hours in any one field):9
Introduction to Anthropology
Introduction to Cultural Anthropology
Biological Anthropology: The Making of Us
Introduction to the Mass Media
Maps and Remote Sensing
Introduction to International Relations
Comparative Government
Introduction to Public Policy
Crime and Justice in America
Introduction to Sociology
Contemporary Social Problems
Core Concentration Courses
Choose 15 hours from the following: 15
Introduction to Forensic Anthropology
Anthropology of International Development
Environment and Society
Plagues and People
Political Communication
Health Communication
White Collar and Computer Crime
Survey of Geospatial Technologies
Urban Geography
State Election Policy and Politics
Public Opinion
Political Behavior
International Conflict and Security
International Terrorism
Political Analysis
Democracy and Inequality
Civil Wars and Intra-State Conflicts
Rural Sociology
Social Organization and Change
Poverty, Analysis: People, Organization and Program
Environment and Society
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23

Psychoinformatics Concentration

Fundamental Discipline Courses
PSY 1021Careers in Psychology1
PSY 3104Introductory Psychological Statistics4
PSY 3314Experimental Psychology4
Core Concentration Courses
Choose 9 hours from the following:9
Psychology of Learning
Social Psychology
Cognitive Psychology
Introduction to Developmental Psychology
Biological Psychology
6 hours of 4000-level PSY courses6
Capstone
PSY 4000Directed Individual Study in Psychology6

Statistical Modeling Concentration

Core Concentration Courses
Choose 24 hours from the following: 24
Introduction to Modern Scientific Computing
Discrete Mathematics
Graph Theory
Mathematical Foundations of Machine Learning
Nonparametric Methods
Data Analysis I
Introduction to Spatial Statistics
Introduction to Mathematical Statistics I
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23

Computational Intelligence Concentration

Core Concentration Courses
CSE 2213Methods and Tools in Software Development3
CSE 4163Designing Parallel Algorithms3
CSE 4293AI for Cybersecurity3
CSE 4623Computational Biology3
CSE 4643AI Robotics3
CSE 4653Cognitive Science3
CSE 4683Machine Learning and Soft Computing3
CSE 4833Introduction to Analysis of Algorithms3
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23

Geoinformatics Concentration

Fundamental Discipline Courses
GR 4303Principles of GIS3
GR 4633Statistical Climatology3
Choose 1 of the following: 3
Remote Sensing of the Physical Environment
Satellite Meteorology
Radar Meteorology
Core Concentration Courses
Choose 15 hours from the following: 15
Urban Geography
Advanced GIS 2
Cartographic Sciences 2
Remote Sensing of the Physical Environment 1, 2
Advanced Remote Sensing in Geosciences 2
Geographic Information Systems Programming 2
Computer Methods in Meteorology
Applied Climatology
Physical Meteorology and Climatology I
Physical Meteorology and Climatology II
Synoptic Meteorology
Satellite Meteorology 1
Radar Meteorology 1
Water Resources
Applied Geophysics
Geomorphology
Coastal Environments
Community Engagement in Environmental Geosciences
Physical Hydrogeology
Capstone
DSCI 4553Data Science Capstone 13
DSCI 4663Data Science Capstone 23
1

Can be used as remaining hours if not already used for the required concentration.

2

Counts towards the Geospatial and Remote Sensing Minor

Sports Science Concentration 

BIO 1004Anatomy and Physiology 14
EP 3233Anatomical Kinesiology3
EP 3304Exercise Physiology4
EP 4504Mechanical Analysis of Movement4
Human Performance Emphasis3
Choose one of the following;
Sport Psychology
Philosophy of Sport & Physical Activity
Core Concentration Courses
PE 4283Sport Biomechanics3
PE 3313Sport Physiology3
EP 4153Training Techniques for Exercise and Sport 23
DSCI 4663Data Science Capstone 23
1

If taken as a general education credit, an additional Sports Science course will be added.

2

Serves as requirement for DSCI 4553 

Bachelor of Science in Healthcare Administration

The Bachelor of Science in Healthcare Administration is housed in the Office of the Provost and Executive Vice President (Academic Affairs) and courses are offered through the Meridian campus.  The degree provides the specialized education needed to equip managers with the skills and knowledge needed to successfully navigate healthcare, finance, the specialized healthcare regulatory environment, and the nuances of management, marketing, operations, and informatics in a healthcare setting.

Graduates are prepared to serve as managers of smaller group practices and as mid-level managers in a wide variety of settings including hospitals, long term care facilities, pharmaceutical agencies, insurance companies, and medical equipment manufacturers. The curriculum also serves as a foundation for success in graduate-level academic programs in health administration and health informatics.

English
EN 1103English Composition I3-4
or EN 1104 Expanded English Composition I
EN 1113English Composition II3
or EN 1173 Accelerated Composition II
Fine Arts
Any Gen Ed course3
Natural Sciences
2 lab based sciences required by Gen Ed6
Math
MA 1613Calculus for Business and Life Sciences I3
or MA 1713 Calculus I
Humanities
Any additional Gen Ed course6
Social/Behavioral Sciences
EC 2113Principles of Macroeconomics3
EC 2123Principles of Microeconomics3
Major Core
HCA 3103Health Information Systems3
HCA 3113Managing Healthcare Organizations3
HCA 3123Healthcare Economics3
HCA 3133Healthcare Logistics & Supply Chain Management3
HCA 3203Healthcare Marketing3
HCA 3313Introduction to the U.S. Healthcare System3
HCA 3513Human Resource Management in Healthcare3
HCA 3813Introduction to Healthcare Law and Regulation3
HCA 4013Ethical Issues in Healthcare3
HCA 4103Quality Management & Process Improvement in Healthcare3
HCA 4123Introduction to Health Informatics3
HCA 4303Financial Management for Healthcare3
HCA 4404Strategic Management for Healthcare4
HCA 4443Healthcare Internship3
HCA 4803Healthcare Policy3
Business Courses
ACC 2013Principles of Financial Accounting3
BQA 3123Business Statistical Methods II3
FIN 3123Financial Management3
Writing Requirement
EN 3313Writing for the Workplace3
or MGT 3213 Organizational Communications
Additional Math Requirement
BQA 2113Business Statistical Methods I3
or ST 2113 Introduction to Statistics
Oral Communication Requirement
CO 1003Fundamentals of Public Speaking3
or CO 1013 Introduction to Communication
Elective Courses
Restricted Electives (Must be upper division, either HCA or related courses, and approved by the departmental academic advisor or program director.)5
Free Electives21
Total Hours120

Bachelor of Applied Science in Healthcare Administration

The Bachelor of Applied Science in Healthcare Administration program is offered to provide opportunities for individuals who have health-related A.A.S. degrees to gain the knowledge and skills needed to manage in clinical settings. Students entering this program are allowed to apply up to 45 hours of their A.A.S. technical coursework toward the B.A.S. degree. 

English
EN 1103English Composition I3-4
or EN 1104 Expanded English Composition I
EN 1113English Composition II3
or EN 1173 Accelerated Composition II
Fine Arts
Any Gen Ed course3
Natural Sciences
2 lab based sciences required by Gen Ed6
Math
BQA 2113Business Statistical Methods I3
or ST 2113 Introduction to Statistics
or equivalent
Humanities
2 General Education Humanities courses
6
Social/Behavioral Sciences
2 General Education Social Sciences courses (EC 2123 recommended)
Major Core
HCA 3103Health Information Systems3
HCA 3113Managing Healthcare Organizations3
HCA 3123Healthcare Economics3
HCA 3203Healthcare Marketing3
HCA 3313Introduction to the U.S. Healthcare System3
HCA 3513Human Resource Management in Healthcare3
HCA 3813Introduction to Healthcare Law and Regulation3
HCA 4013Ethical Issues in Healthcare3
HCA 4303Financial Management for Healthcare3
HCA 4803Healthcare Policy3
PCS Courses
PCS 2111Introduction to the Bachelor of Applied Science1
PCS 3103Professional Leadership Strategies3
PCS 4112Professional Success Strategies in Applied Fields2
Additional Required Courses
CO 1003Fundamentals of Public Speaking3
or CO 1013 Introduction to Communication
EN 3313Writing for the Workplace *3
or MGT 3213 Organizational Communications
FIN 3123Financial Management3
Technical Courses in Discipline45
Total Hours120
*

Meets Jr/Sr Writing Course requirement

Data Science Minor

The Data Science minor will provide students with an overview of data science as both a field of study and an industry sector.  The minor draws form courses within the 64 hours of core Bachelor of Science in Data Science coursework. Students will complete four courses that provide: an introduction to the field, preparation in computer programming, an introduction to data visualization, and specific instruction in statistics. Three labs will provide hands-on experience in managing and using data throughout the data lifecycle as students work with real-world data. Because data science is usually practiced within a specific subject matter area, students are also encouraged to work within their major departments to identify a research methodology or analysis class that will provide subject matter-specific instruction in applying data science.

CSE 1284Introduction to Computer Programming4
DSCI 2012Data Science Lab: Data Wrangling2
DSCI 2013Data Science Literacy3
DSCI 4013Data Visualization3
MA /ST 3123Introduction to Statistical Inference3
Choose two of the following: 4
Data Science Lab: Cloud, High-Performance, and Quantum Computing
Data Science Lab: Description, Analysis, and Inference
Data Science Lab: Data Visualization
Data Science Lab: Artificial Intelligence
Total Hours19

Geospatial and Remote Sensing Minor

Technology revolutions have driven the expectations of remote sensing and geospatial technologies to an all-time high for a new generation of users across a vast number of disciplines. Advances in computational technologies, visualization products, and sensor technologies have led to the development of unprecedented capabilities in geospatial technologies, such as remote sensing and geographic information systems. With the plethora of remote sensing technologies, the industry is poised to develop operational remote sensing applications that fundamentally impact management of resources. Mississippi State University has developed broad, multi-disciplinary efforts in spatial technologies of many types, and is a leader among universities in education and outreach activities to prepare the next generation for utilizing these technologies. One of the primary limitations in the development of this industry is the need for a better-educated workforce that can understand and utilize the tools of these spatial technologies. Education in geospatial and remote sensing technologies is by nature multi-disciplinary; therefore, a minor program that crosses departmental and college boundaries has been developed to address these needs. This undergraduate minor can thus serve the needs of MSU students with diverse backgrounds from a variety of disciplines. Students may strategically assess which courses within their disciplinary academic program can be used for the minor, thus satisfying the needs of both and maximizing their education experience.

The minor should represent a student’s mastery of basic GIS and Remote Sensing coursework. A minimum of 3 hours of coursework is required in each of these areas:

  • Geographic Information Systems
  • Remote Sensing
  • Advanced Geospatial Technologies

Students are required to complete 6 hours of additional coursework within the category of Geospatial Applications. A list of geospatial application electives is listed, and it includes courses that are offered by several MSU departments.

Due to the multi-disciplinary nature of this program, the Office of the Academic Affairs is the resident office for admission and administration. Thus, the program is not focused on a single college or department. A program coordinator, appointed by the Provost, advises students seeking the GRS minor, and assists departmental advisors. The coordinator is also responsible for conducting the necessary transcript audits and authorizing the awarding of the minor.

For further information and enrollment information, contact the GRS program coordinator:

Dr. John Rodgers
Department of Geosciences
355 Lee Blvd, 108 Hilbun Hall
Mississippi State, MS 39762
662-325-3915, jcr100@msstate.edu

A total of 15 semester hours are required: nine selected from the list of required courses, and six selected from the list of elective courses.

Required Courses
Remote Sensing
Choose one of the following:3
Introduction to Remote Sensing Technologies
Introduction to Remote Sensing Technologies
Remote Sensing of the Physical Environment
Remote Sensing Applications
GIS
Choose one of the following:3
Principles of GIS
Application of Spatial Technologies to Wildlife and Fisheries Management
FO 4472/6472
AND
FO 4471/6471
Advanced Geospatial Coursework
Choose one of the following:3
Spatial Technologies in Natural Resources Management
Spatial Statistics for Natural Resources
Ecological Modeling in Natural Resources
Advanced Spatial Technologies
Advanced GIS
Advanced Remote Sensing in Geosciences
Advanced Geodatabase Systems
Introduction to Spatial Statistics
Geospatial Applications
Choose two of the following:(Courses must be different from the ones taken from the above categories. A course may not be used to satisfy more than one requirement)6
The Global Positional System and Geographic Information Systems in Agriculture and Engineering
Signals and Systems
Digital Signal Processing
Current Topics in Remote Sensing
Digital Image Processing
Spatial Technologies in Natural Resources Management
Advanced Spatial Technologies
Spatial Statistics for Natural Resources
Ecological Modeling in Natural Resources
Survey of Geospatial Technologies
Advanced GIS
Cartographic Sciences
Advanced Remote Sensing in Geosciences
Geodatabase Design
Geographic Information Systems Programming
Geospatial Agronomic Management
Remote Sensing Seminar
Remote Sensing Seminar
Introduction to Spatial Statistics
Total Hours15

Leadership Studies Minor

The interdisciplinary minor in Leadership Studies provides academic and experiential knowledge and skills to prepare students for future leadership positions in communities, professions, and organizations. The Leadership Studies minor is open to Mississippi State University students in all Colleges, Schools, and majors. It requires 19 hours of approved coursework, including at least one experiential internship component. No more than two courses from the same academic Department may be applied to this minor. Students in the Leadership Studies minor must maintain a grade point average of 2.00 or higher overall and a grade point average of 2.50 or higher in courses applied to the minor. Students must earn a grade of C or higher in all minor courses.

Admission and Graduation Standards: Entering freshmen may declare a Leadership Studies minor in the first semester by securing approval of a minor program of studies as outlined herein. Qualified students, including incoming transfer students, may declare the minor during any subsequent semester. After the first semester of college, students must have a minimum overall GPA of 2.00 or higher (including all course work taken, not just in the minor) to enter or remain in the minor. To graduate with a Minor in Leadership Studies, students must meet all course requirements on their approved programs of minor study, must have an overall GPA of 2.00 or higher on all coursework attempted, and must have a 2.50 or higher GPA over all minor courses. Students must earn grades of C or higher in all courses applied to the Leadership Studies minor.

Curriculum Outline: Each student will select one core course in each of three core areas: Ethics, which are essential for any leader; Social Science, which studies leadership directly and provides knowledge of direct relevance to leadership; and Communication, which involves skills that are critically important for leaders. (For students in majors with little room for electives, judicious selection of the core courses in the Leadership Studies minor may simultaneously fulfill certain General Education requirements, College or School Core Curriculum, or Departmental Major requirements.) Each student will further select from an approved list, in consultation with his or her Leadership Studies minor advisor, at least three more courses that facilitate the student’s goals. Finally, each student will register for a 1-hour (48 contact hours) experiential internship.

Area I: Ethics and Leadership
Choose one of the following:3
Introduction to Ethics
Socially Responsible Leadership
Area II: Leadership and Social Science
Choose one of the following:3
Organizational Behavior
Social Psychology
Political Leadership
Introduction to Engineering and Public Policy
Area III: Leadership and Communication Skills
Choose one of the following:3
Fundamentals of Public Speaking
Small Group Communication
Principles of Public Relations
Area IV: Experiential internship component
EXL 1191Leadership Studies Internship I1
Area V: Electives
Choose a minimum of three:9
See advisor for a complete list of approved leadership electives. Courses listed in the Minor Core may also be taken as electives if they are not being used to satisfy the minor core requirement. Students generally take all of their electives in the same college, but doing so is not a requirement. Elective are best selected in consultation with the student's Leadership Studies Minor advisor to meet the goals and objectives of the student. Electives are available in each college which allows this minor to be applicable to any major.

For additional information, contact Robert Green, Chair, Leadership Studies Minor committee at green@bagley.msstate.edu

DSCI 2012 Data Science Lab: Data Wrangling: 2 hours.

Four hours laboratory. Practical application of data science tools to clean, format, and work with data

DSCI 2013 Data Science Literacy: 3 hours.

Three hours lecture. Introduction to data science as a field that represents the world and society through data objects, extracts new knowledge from these data objects, and creates artificially intelligent systems that perform tasks while producing further insights that improve the performance of institutions, organizations, businesses, and society

DSCI 2022 Data Science Lab: Cloud, High-Performance, and Quantum Computing: 2 hours.

(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of cloud GPU/TPU, high-performance, or quantum computing platforms to perform computing tasks for big data analysis tasks

DSCI 3012 Data Science Lab: Description, Analysis, and Inference: 2 hours.

(Prerequisite: MA 3123). Four hours laboratory. Hands-on programming work to use descriptive, inferential, predictive, and prescriptive statistical techniques with a variety of data types

DSCI 3013 Fundamentals of Data Acquisition: 3 hours.

Three hours lecture. An introduction to the fundamentals of data and data acquisition for data science

DSCI 3022 Data Science Lab: Data Visualization: 2 hours.

(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of tools and programming libraries to visualize data using common approaches to the visual display of numerical, conceptual, and geospatial information

DSCI 3032 Data Science Lab: Artificial Intelligence: 2 hours.

(Prerequisite: DSCI 2012). Four hours laboratory. Hands-on use of artificial intelligence and machine-learning libraries to train models in areas such as natural language processing, computer vision, and classification

DSCI 4000 Directed Individual Study in Data Science: 1-6 hours.

Hours and credits to be arranged

DSCI 4013 Data Visualization: 3 hours.

Three hours lecture. Course providing theoretical foundation for data visualization. Deals with external representation and interactive manipulation of information, data, or artifacts using digital tools to enhance communication, analytical reasoning, and decision-making. (Same as CSE 4423)

DSCI 4553 Data Science Capstone 1: 3 hours.

(Prerequisite: Senior Standing). The first of two, three-hour capstone courses. An individual, project-based course open only to candidates for the Bachelor of Science in Data Science degree. Formal written and oral project reports are required

DSCI 4663 Data Science Capstone 2: 3 hours.

(Prerequisites: Senior Standing, DSCI 4553). The second of two, three-hour capstone courses. An individual, project-based course open only to candidates for the Bachelor of Science in Data Science degree. Formal written and oral project reports are required

DSCI 6113 Programming for Applied Data Science: 3 hours.

One hour lecture, two hours laboratory. Computer programming and data wrangling through practical application of Python and other data science tools to clean, format, and work with real datasets

DSCI 6122 R Lab for Applied Data Science: 2 hours.

Two hours laboratory. Introduction to programming, data wrangling, and data exploration through practical application of R and associated tools to clean, format, and work with real datasets from various fields

DSCI 6133 Applied Data Visualization: 3 hours.

(Prerequisite: DSCI 6113 Programming for Applied Data Science). One hour lecture, two hours laboratory. Explore and understand data visually, communicate meaning visually, and create interactive visualizations using industry-standard tools and programming languages

DSCI 6204 Applied Statistical Methods for Data Science: 4 hours.

(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Select and apply appropriate statistical methods and data science technologies to achieve analytical objectives. Write code to apply descriptive, inferential, predictive, and prescriptive statistical techniques for a variety of data types and purposes

DSCI 6214 Applied Machine Learning for Data Science: 4 hours.

(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Select and apply appropriate machine learning methods and data science technologies to implement and optimize non-artificial neural network approaches to inference, planning, and classification projects. Learn to use GPUs for computing and estimation

DSCI 6301 Data Science Project Management: 1 hour.

One hour Lecture. A practical introduction to project management in the context of data science projects

DSCI 7000 Directed Individual Study in Data Science: 1-6 hours.

Hours and credits to be arranged

DSCI 8013 Data Science Literacy Pedagogy 1: Governance, Ethics, and Data Science Applications: 3 hours.

Three hours lecture. General subject-matter introduction to the field of data science and data science instruction with a focus on governance, ethics, and data science applications in many fields

DSCI 8023 Data Science Literacy Pedagogy 2: Technical Overview of Data Science Methods & Strategies: 3 hours.

Three hours lecture. General subject-matter introduction to the field of data science and data science instruction with a focus on data science methods and strategies

DSCI 8033 Data Science Classroom Integration: 3 hours.

Three hours lecture. Applying and integrating principles of data science into the context of the classroom. Topics include importance of data science across the domain; digital citizenship; career exploration; and an historical perspective on analyzing, posing, and solving problems using data

DSCI 8133 Foundations of Applied Data Science I: 3 hours.

Three hours lecture. Introduction to data science as a field that advances methods to improve the use of data for human progress

DSCI 8143 Foundations of Applied Data Science II: 3 hours.

Three hours lecture. In-depth engagement with all phases of the data science lifecycle including data modeling and acquisition, storage, analysis, and building smart systems

DSCI 8224 Applied Neural Networks and Deep Learning for Data Science: 4 hours.

(Prerequisite: DSCI 6113 Programming for Applied Data Science). Two hours lecture, two hours laboratory. Learn the fundamentals of artificial neural networks. Use AI/ML libraries to implement artificial neural network approaches to computer vision, natural language processing, reinforcement learning, and generative projects. Deepen ability to use GPUs for computing

DSCI 8413 Applied Graduate Data Science Capstone: 3 hours.

Three hours lecture. Faculty-directed capstone for the Master of Applied Data Science program in which students use the principles and practices of data science to address a challenge within the student's subject area focus

DSCI 8990 Introduction to Data Science Literacy Instruction: 1-9 hours.

Credit and title to be arranged. This course is to be used on a limited basis to offer developing subject matter areas not covered in existing courses. (Courses limited to two offerings under one title within two academic years)