Office: 3501 Lee Hall
662-325-3742
P.O. Box BQ; Mississippi State, MS 39762
Office of Academic Affairs
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.
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. Students must earn a grade of C or higher in the two-semester senior capstone courses included in each of the concentrations.
| English | ||
| EN 1103 | English Composition I | 3-4 |
| or EN 1104 | Expanded English Composition I | |
| EN 1113 | English Composition II | 3 |
| or EN 1173 | Accelerated Composition II | |
| Fine Arts | ||
| Any Gen Ed course | 3 | |
| Natural Sciences | ||
| 2 lab based sciences required by Gen Ed | 6 | |
| Math | ||
| MA 1713 | Calculus I | 3 |
| Humanities | ||
| PHI 1113 | Introduction to Logic | 3 |
| Any additional Gen Ed course | 3 | |
| Social/Behavioral Sciences | ||
| DSCI 2013 | Data Science Literacy | 3 |
| Any additional Gen Ed course | 3 | |
| Oral Communication | ||
| CO 3213 | Small Group Communication | 3 |
| Technical Writing | ||
| CO 3223 | Communication & Media Research Methods | 3 |
| Major Core | ||
| MA 1723 | Calculus II | 3 |
| MA 2733 | Calculus III | 3 |
| MA 3123 | Introduction to Statistical Inference | 3 |
| MA 3113 | Introduction to Linear Algebra | 3 |
| MA 4523 | Introduction to Probability | 3 |
| or ST 4523 | Introduction to Probability | |
| CSE 1284 | Introduction to Computer Programming | 4 |
| CSE 1384 | Intermediate Computer Programming | 4 |
| CSE 2813 | Discrete Structures | 3 |
| CSE 2383 | Data Structures and Analysis of Algorithms | 3 |
| CSE 3763 | Ethical and Legal Issues in Computing | 3 |
| CSE 4503 | Database Management Systems | 3 |
| CSE 4633 | Artificial Intelligence | 3 |
| BIS 3233 | Management Information Systems | 3 |
| DSCI 2012 | Data Science Lab: Data Wrangling | 2 |
| DSCI 2022 | Data Science Lab: Cloud, High-Performance, and Quantum Computing | 2 |
| DSCI 3012 | Data Science Lab: Description, Analysis, and Inference | 2 |
| DSCI 3013 | Fundamentals of Data Acquisition | 3 |
| DSCI 3022 | Data Science Lab: Data Visualization | 2 |
| DSCI 3032 | Data Science Lab: Artificial Intelligence | 2 |
| DSCI 4013 | Data Visualization | 3 |
| Concentration Courses | 30 | |
| Total Hours | 123 | |
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 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
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 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
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 | ||
MKT 3323 | ||
| Core Concentration Courses | ||
| BQA 4423 | Business Decision Analysis | 3 |
| BIS 4533 | Decision Support Systems | 3 |
| BIS 4113 | Business Information Systems Security Management | 3 |
| BIS 4753 | Structured Systems Analysis and Design | 3 |
| 4000-level business course | 3 | |
| Non-business course from any of the data science concentrations | 3 | |
| Capstone | ||
| BIS 4763 | BIS Senior Seminar | 3 |
| BQA 4413 | Business Forecasting and Predictive Analytics | 3 |
Marketing and Supply Chain Concentration
| Fundamental Discipline Courses | ||
| BQA 4423 | Business Decision Analysis | 3 |
| MKT 3013 | Principles of Marketing | 3 |
| MKT 3323 | ||
| 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 | ||
MKT 4013 | ||
| International Transportation | ||
| Internet Marketing | ||
MKT 4313 | ||
| 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 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
Psychoinformatics Concentration
| Fundamental Discipline Courses | ||
| PSY 1021 | Careers in Psychology | 1 |
| PSY 3104 | Introductory Psychological Statistics | 4 |
| PSY 3314 | Experimental Psychology | 4 |
| 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 courses | 6 | |
| Capstone | ||
| PSY 4000 | Directed Individual Study in Psychology | 6 |
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 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
Computational Intelligence Concentration
| Core Concentration Courses | ||
| CSE 2213 | Methods and Tools in Software Development | 3 |
| CSE 3183 | Systems Programming | 3 |
| CSE 4293 | AI for Cybersecurity | 3 |
| CSE 4623 | Computational Biology | 3 |
| CSE 4643 | AI Robotics | 3 |
| CSE 4653 | Cognitive Science | 3 |
| CSE 4683 | Machine Learning and Soft Computing | 3 |
| CSE 4833 | Introduction to Analysis of Algorithms | 3 |
| Capstone | ||
| DSCI 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
Geoinformatics Concentration
| Fundamental Discipline Courses | ||
| GR 4303 | Principles of GIS | 3 |
| GR 4633 | Statistical Climatology | 3 |
| 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 4553 | Data Science Capstone 1 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
- 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 1004 | Anatomy and Physiology 1 | 4 |
| EP 3233 | Anatomical Kinesiology | 3 |
| EP 3304 | Exercise Physiology | 4 |
| EP 4504 | Mechanical Analysis of Movement | 4 |
| Human Performance Emphasis | 3 | |
| Choose one of the following; | ||
| Sport Psychology | ||
| Philosophy of Sport & Physical Activity | ||
| Core Concentration Courses | ||
| PE 4283 | Sport Biomechanics | 3 |
| PE 3313 | Sport Physiology | 3 |
| EP 4153 | Training Techniques for Exercise and Sport 2 | 3 |
| DSCI 4663 | Data Science Capstone 2 | 3 |
- 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 1103 | English Composition I | 3-4 |
| or EN 1104 | Expanded English Composition I | |
| EN 1113 | English Composition II | 3 |
| or EN 1173 | Accelerated Composition II | |
| Fine Arts | ||
| Any Gen Ed course | 3 | |
| Natural Sciences | ||
| 2 lab based sciences required by Gen Ed | 6 | |
| Math | ||
| MA 1613 | Calculus for Business and Life Sciences I | 3 |
| or MA 1713 | Calculus I | |
| Humanities | ||
| Any additional Gen Ed course | 6 | |
| Social/Behavioral Sciences | ||
| EC 2113 | Principles of Macroeconomics | 3 |
| EC 2123 | Principles of Microeconomics | 3 |
| Major Core | ||
| HCA 3103 | Health Information Systems | 3 |
| HCA 3113 | Managing Healthcare Organizations | 3 |
| HCA 3123 | Healthcare Economics | 3 |
| HCA 3133 | Healthcare Logistics & Supply Chain Management | 3 |
| HCA 3203 | Healthcare Marketing | 3 |
| HCA 3313 | Introduction to the U.S. Healthcare System | 3 |
| HCA 3513 | Human Resource Management in Healthcare | 3 |
| HCA 3813 | Introduction to Healthcare Law and Regulation | 3 |
| HCA 4013 | Ethical Issues in Healthcare | 3 |
| HCA 4103 | Quality Management & Process Improvement in Healthcare | 3 |
| HCA 4123 | Introduction to Health Informatics | 3 |
| HCA 4303 | Financial Management for Healthcare | 3 |
| HCA 4404 | Strategic Management for Healthcare | 4 |
| HCA 4443 | Healthcare Internship | 3 |
| HCA 4803 | Healthcare Policy | 3 |
| Business Courses | ||
| ACC 2013 | Principles of Financial Accounting | 3 |
| BQA 3123 | Business Statistical Methods II | 3 |
| FIN 3123 | Financial Management | 3 |
| Writing Requirement | ||
| EN 3313 | Writing for the Workplace | 3 |
| or MGT 3213 | Organizational Communications | |
| Additional Math Requirement | ||
| BQA 2113 | Business Statistical Methods I | 3 |
| or ST 2113 | Introduction to Statistics | |
| Oral Communication Requirement | ||
| CO 1003 | Fundamentals of Public Speaking | 3 |
| 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 Electives | 21 | |
| Total Hours | 120 | |
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 1103 | English Composition I | 3-4 |
| or EN 1104 | Expanded English Composition I | |
| EN 1113 | English Composition II | 3 |
| or EN 1173 | Accelerated Composition II | |
| Fine Arts | ||
| Any Gen Ed course | 3 | |
| Natural Sciences | ||
| 2 lab based sciences required by Gen Ed | 6 | |
| Math | ||
| BQA 2113 | Business Statistical Methods I | 3 |
| 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 3103 | Health Information Systems | 3 |
| HCA 3113 | Managing Healthcare Organizations | 3 |
| HCA 3123 | Healthcare Economics | 3 |
| HCA 3203 | Healthcare Marketing | 3 |
| HCA 3313 | Introduction to the U.S. Healthcare System | 3 |
| HCA 3513 | Human Resource Management in Healthcare | 3 |
| HCA 3813 | Introduction to Healthcare Law and Regulation | 3 |
| HCA 4013 | Ethical Issues in Healthcare | 3 |
| HCA 4303 | Financial Management for Healthcare | 3 |
| HCA 4803 | Healthcare Policy | 3 |
| PCS Courses | ||
| PCS 2111 | Introduction to the Bachelor of Applied Science | 1 |
| PCS 3103 | Professional Leadership Strategies | 3 |
| PCS 4112 | Professional Success Strategies in Applied Fields | 2 |
| Additional Required Courses | ||
| CO 1003 | Fundamentals of Public Speaking | 3 |
| or CO 1013 | Introduction to Communication | |
| EN 3313 | Writing for the Workplace * | 3 |
| or MGT 3213 | Organizational Communications | |
| FIN 3123 | Financial Management | 3 |
| Technical Courses in Discipline | 45 | |
| Total Hours | 120 | |
- *
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 1284 | Introduction to Computer Programming | 4 |
| DSCI 2012 | Data Science Lab: Data Wrangling | 2 |
| DSCI 2013 | Data Science Literacy | 3 |
| DSCI 4013 | Data Visualization | 3 |
| MA /ST 3123 | Introduction to Statistical Inference | 3 |
| 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 Hours | 19 | |
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 Hours | 15 | |
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 1191 | Leadership Studies Internship I | 1 |
| 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)