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- Bachelor of Science in Data Science
Bachelor of Science in Data Science
Creating Data Driven Problem Solvers
Students earning a bachelor's degree in data science will take courses in programming, statistics, analytics, database, and machine learning as well as selecting a cognate in either Inferential Thinking, Business Intelligence, or Advanced Machine Learning. Students will also be able to put those skills to use through 'real-world' projects. A degree that truly makes you career ready through courses and experiences to create data driven problem solvers.
Students will also be able to put those skills to use through ‘real-world’ projects. A degree that truly makes you career ready through courses and experiences to create data driven problem solvers
For more information email datascience@mtsu.edu.
What We're Doing
MTSU receives Nashville Technology Council awards
Middle Tennessee State University was well represented at the 2020 Nashville Technology Council Awards, bringing home three of the major awards for the evening, “Data Scientist of the Year,” “Technology Student of the Year,” and “Diversity and Inclusion Initiative of the Year.”
Related Media
Careers directly after graduation (entry level)
- Business Analyst
- Business Intelligence Analyst
- Data Analyst
- Data Analytics Consultant
- Data Architect
- Data Engineer
- Data Infrastructure Engineer
- Data Mining Engineer
- Database Administrator
- Junior Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
- Software Developer
- Systems Analyst
Career path with experience or additional education
- AI Engineer
- Data Scientist
- Quantitative Researcher
- Statistician
Notable Companies looking to hiring in Data Science:
- Accenture
- Asurion
- Bank of New York Mellon
- Bridgestone
- CAT Financial
- CGI
- Change Healthcare
- Deloitte
- Digital Reasoning
- Dollar General
- EFC Systems
- Genesco
- Homesite Insurance
- Hospital Corporation of America (HCA)
- HPA Cognizant Technologies
- Ingram Content Group
- Ludi, Inc.
- Nashville SC (Professional soccer)
- Nissan
- State of Tennessee
- Stratasan
- The General
- Tractor Supply
- Trinisys
- Vaco
- Vanderbilt Medical
MTSU offers a Bachelor of Science degree in Data Science with three cognates, Inferential Thinking, Business Intelligence, or Machine Learning. The B.S. also allows a student 15 general elective hours that may be used toward a minor or any other courses that the student desires to incorporate with their Data Science degree.
For complete curriculum details, click on the REQUIREMENTS button to the right.
Minor
A Data Science minor gives you the data skills to add to your current major to allow you to be a data-driven problem solver. There are 5 required courses (16 credit hours)
Graduate Certificate
For graduate students, a graduate certificate in Data Science is offered. This includes the four courses listed below that are mostly online courses. Each course is 7 weeks long and the whole certificate can be completed in two semesters.
Master’s degree
A Master of Science in Data Science is available.
Ph.D. in Computational and Data Science
A Ph.D. in Computational and Data Science is also available.
For more information email datascience@mtsu.edu
Data Science, B.S.
Lisa Green, program coordinator
Lisa.Green@mtsu.edu
615-898-5775
Data Science is an interdisciplinary field that covers the use of data to make decisions, gain insight, or develop knowledge. Data scientists combine skills from computer science, statistics, and business analytics. Students will start from a business understanding of the question at hand, using it to inform and understand the data available, then use skills in the preparation and display of data and in the modeling of data to evaluate the issue at hand. Finally, they will deploy the model they created in order to ensure that it is widely used. A capstone project or internship will allow students to follow the process of data science in a real-world setting and will ensure that they have a portfolio of work to show prospective employers.
Academic Map
Following is a printable, suggested four-year schedule of courses:
Data Science, B.S., Academic Map
Degree Requirements
| General Education | 41 hours |
| Major Requirements | 45 hours |
| Data Science Electives | 12 hours |
| Supporting Courses | 7 hours* |
| Electives | 15-21 hours |
| TOTAL | 120 hours |
*This program requires courses that can also fulfill requirements of the General Education curriculum. If program requirements are also used to fulfill General Education requirements, the number of elective hours may vary.
General Education (41 hours)
General Education requirements (shown in curricular listings below) include courses in Communication, History, Humanities and/or Fine Arts, Mathematics, Natural Sciences, and Social/Behavioral Sciences.
The following courses required by the program meet General Education requirements:
Major Requirements (45 hours)
- BIA 3620 - Introduction to Business Analytics 3 credit hours
BIA 3620 - Introduction to Business Analytics
3 credit hours
Prerequisites: BIA 2610 or MATH 1530, junior standing. Introduces the concepts and application of data analytics in business. Spreadsheet software and associated analytic tools utilized to visualize, model, and analyze business data using a hands-on-approach.
- CSCI 1170 - Computer Science I 4 credit hours
CSCI 1170 - Computer Science I
4 credit hours
Prerequisite: MATH 1730 or MATH 1810 with a grade of C or better or Math ACT of 26 or better or Calculus placement test score of 73 or better. The first of a two-semester sequence using a high-level language; language constructs and simple data structures such as arrays and strings. Emphasis on problem solving using the language and principles of structured software development. Three lecture hours and two laboratory hour.
- CSCI 2170 - Computer Science II 4 credit hours
CSCI 2170 - Computer Science II
4 credit hours
Prerequisites: CSCI 1170 (or equivalent) with a grade of C or better and MATH 1730 or MATH 1810 with a grade of C or better or Math ACT of 26 or better or Calculus placement test score of 73 or better. A continuation of CSCI 1170. Topics include introductory object-oriented programming techniques, software engineering principles, records, recursion, pointers, stacks and queues, linked lists, trees, and sorting and searching. Three lecture hours and two laboratory hours.
- DATA 1500 - Introduction to Data Science 3 credit hours
DATA 1500 - Introduction to Data Science
3 credit hours
(Same as BIA 1500.) Introduces basic principles and tools as well as its general mindset in data science. Concepts on how to solve a problem with data include business and data understanding, data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
- DATA 3500 - Data Cleansing and Feature Engineering 3 credit hours
DATA 3500 - Data Cleansing and Feature Engineering
3 credit hours
Prerequisite: CSCI 1170. Techniques and applications used to collect and integrate data, inspect the data for errors, visualize and summarize the data, clean the data, and prepare the data for modeling for various data types.
- DATA 3550 - Applied Predictive Modeling 3 credit hours
DATA 3550 - Applied Predictive Modeling
3 credit hours
(Same as STAT 3550.) Prerequisite: CSCI 1170. An overview of the modeling process used in data science. Covers the ethics involved in data science, data preprocessing, regression models, classification models, and presenting the model.
- DATA 4950 - Data Science Capstone 3 credit hours
DATA 4950 - Data Science Capstone
3 credit hours
Prerequisites: Senior standing; Data Science major; DATA 3500 and DATA 3550. A project-based course that will utilize data science skills to prepare, display, model, analyze, and present data to solve a real-world problem.
- INFS 4790 - Database Design and Development 3 credit hours
INFS 4790 - Database Design and Development
3 credit hours
Prerequisites: INFS 2600, ISA major, junior standing, and admission into the College of Business. Fundamental concepts: conventional data systems, integrated management information systems, database structure systems, data integration, complex file structure, online access systems. Emphasis on total integrated information systems database and database management languages.
- MATH 2110 - Data Analysis 1 credit hour
MATH 2110 - Data Analysis
1 credit hour
Prerequisite or corequisite: MATH 1530 or MATH 2050 or equivalent. Using computer software for graphing and analysis of scientific and statistical data.
- MATH 2530 - Applied Statistics II 3 credit hours
MATH 2530 - Applied Statistics II
3 credit hours
Prerequisite: MATH 1530 or MATH 2050 or equivalent. Explores the application of the following statistical methods: analysis of variance, simple and multiple regression models, categorical data analysis, and nonparametric methods. Three hours lecture per week.
- BIA 2610 - Statistical Methods 3 credit hours OR
BIA 2610 - Statistical Methods
3 credit hours
The application of collecting, summarizing, and analyzing data to make business decisions. Topics include measures of central tendency, variation, probability theory, point and interval estimation, correlation and regression. Computer applications emphasized.
- MATH 1530 - Applied Statistics 3 credit hours OR
MATH 1530 - Applied Statistics
3 credit hours
Prerequisites: Two years of high school algebra and a Math Enhanced ACT 19 or greater or equivalent. Descriptive statistics, probability, and statistical inference. The inference unit covers means, proportions, and variances for one and two samples, and topics from one-way ANOVA, regression and correlation analysis, chi-square analysis, and nonparametrics. TBR Common Course: MATH 1530
- MATH 2050 - Probability and Statistics 3 credit hours
MATH 2050 - Probability and Statistics
3 credit hours
Prerequisite: MATH 1810 or MATH 1910. Data analysis, probability, and statistical inference. The inference material covers means, proportions, and variances for one and two samples, one-way ANOVA, regression and correlation, and chi-square analysis. TBR Common Course: MATH 2050
Data Science Cognate (12 hours)
Choose one cognate from the following:
Inferential Thinking
- MATH 2010 - Elements of Linear Algebra 3 credit hours
MATH 2010 - Elements of Linear Algebra
3 credit hours
Prerequisite: MATH 1910. Vectors and vector spaces, matrices and systems of linear equations, geometry of vector spaces and linear transformations in a vector space.
- STAT 4360 - Regression Analysis 3 credit hours
STAT 4360 - Regression Analysis
3 credit hours
Prerequisite: MATH 2050 or equivalent. Theory and application of regression models. Approaches to model building and data analysis. Computation and interpretation of results facilitated through the use of statistical software packages.
- STAT 4380 - Experimental Design 3 credit hours
STAT 4380 - Experimental Design
3 credit hours
Prerequisite: MATH 2050 or equivalent. Topics include one-way analysis of variances, multiple comparison, multifactor analysis of variance, and various practical issues in experimental design. Computation and interpretation of results facilitated through the use of statistical software packages.
- STAT 4700 - Analysis of Large-Scale Data Sets 3 credit hours
STAT 4700 - Analysis of Large-Scale Data Sets
3 credit hours
Prerequisites: CSCI 1170 and MATH 2530. The analysis and applications of large-scale data sets. Scalable machine learning and data mining applications in a practical clinical environment. Statistical software used in the application of these techniques.
Business Intelligence and Analytics
- BIA 3470 - Python for Business Applications 3 credit hours OR
BIA 3470 - Python for Business Applications
3 credit hours
(Same as INFS 3470.) Introduces Python, a popular, general purpose programming and scripting language well suited to a wide range of business problems. Topics include basics of programming-variables, strings, lists, functions, writing scripts that automate tedious tasks, parsing and interpreting data, interacting with APIs, and building web scrapers. Emphasis on practical applications in a business context.
- INFS 3470 - Python for Business Applications 3 credit hours
INFS 3470 - Python for Business Applications
3 credit hours
(Same as BIA 3470.) Introduces Python, a popular, general purpose programming and scripting language well suited to a wide range of business problems. Topics include basics of programming-variables, strings, lists, functions, writing scripts that automate tedious tasks, parsing and interpreting data, interacting with APIs, and building web scrapers. Emphasis on practical applications in a business context.
- BIA 4010 - Business Analytics and Visualization 3 credit hours
BIA 4010 - Business Analytics and Visualization
3 credit hours
Prerequisite: BIA 3620 or an equivalent course; junior or senior standing; and admission into College of Business. Development and application of industry-level analytic tools to visualize, model, and analyze business data. Opportunity to develop skills for self-service business analytics via hands-on approach.
- INFS 3900 - Business Data Communications 3 credit hours
INFS 3900 - Business Data Communications
3 credit hours
Prerequisites: 6 hours of information systems; junior standing; admission into the College of Business. Practical explanation of data communications technologies and basic applications for business. Includes projects to develop a prototype network in a lab environment for hands-on experience.
- STAT 4700 - Analysis of Large-Scale Data Sets 3 credit hours
STAT 4700 - Analysis of Large-Scale Data Sets
3 credit hours
Prerequisites: CSCI 1170 and MATH 2530. The analysis and applications of large-scale data sets. Scalable machine learning and data mining applications in a practical clinical environment. Statistical software used in the application of these techniques.
Machine Learning
- CSCI 3080 - Discrete Structures 3 credit hours
CSCI 3080 - Discrete Structures
3 credit hours
(Same as MATH 3080.) Prerequisites: CSCI 1170 and MATH 1910 or consent of instructor. Topics include formal logic, proof techniques, matrices, graphs, formal grammars, finite state machines, Turing machines, and binary coding schemes.
- CSCI 3110 - Algorithms and Data Structures 3 credit hours
CSCI 3110 - Algorithms and Data Structures
3 credit hours
Prerequisites: CSCI 2170 and CSCI 3080 with C or better. Topics include additional object-oriented programming techniques, algorithm design, analysis of algorithms, advanced tree structures, indexing techniques, internal and external sorting, graphs, and file organizations.
- CSCI 4350 - Introduction to Artificial Intelligence 3 credit hours
CSCI 4350 - Introduction to Artificial Intelligence
3 credit hours
Prerequisites: CSCI 3110 and CSCI 3080 or equivalent. Principles include search strategies, knowledge representation, reasoning, and machine learning. Applications include expert systems and natural language understanding.
- CSCI 4850 - Neural Nets 3 credit hours
CSCI 4850 - Neural Nets
3 credit hours
Prerequisite: CSCI 3080. Various neural net architectures, theory, and applications including models such as Perceptron, back propagation, Kohonen, ART, and associative memory. Learning and conditioning methods also studied.
Data Science Electives (12 hours)
- CSCI 3130 - Assembly and Computer Organization 4 credit hours
CSCI 3130 - Assembly and Computer Organization
4 credit hours
Prerequisite: CSCI 2170. Assembly language and the organization and basic architecture of computer systems. Topics include hardware components of digital computers, microprogramming, and memory management. Laboratory exercises involve logical, functional properties of components from gates to microprocessors. Three lectures and one two-hour laboratory.
- CSCI 3240 - Introduction to Computer Systems 4 credit hours
CSCI 3240 - Introduction to Computer Systems
4 credit hours
Prerequisites: CSCI 2170 and either CSCI 3130 or ET 3620. Provides a programmer's view of how computer systems execute programs, store information, and communicate. Topics include machine-level code and its generation by optimizing compilers, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation. Three lecture hours and one two-hour laboratory.
- CSCI 4300 - Data Communication and Networks 3 credit hours
CSCI 4300 - Data Communication and Networks
3 credit hours
Prerequisite: CSCI 3240 or CSCI 3250. Computer network architectures, protocol hierarchies, and the open systems interconnection model. Modeling, analysis, design, and management of hardware and software on a computer network.
- CSCI 4330 - Parallel Processing Concepts 3 credit hours
CSCI 4330 - Parallel Processing Concepts
3 credit hours
Prerequisites: CSCI 3130 and CSCI 3240 or CSCI 3250. Basic concepts in parallel processing and programming in a parallel environment. Topics include classification of parallel architectures, study of actual parallel architectures, design and implementation of parallel programs, parallel software engineering.
- DATA 4500 - Internship in Data Science 1 to 3 credit hours
DATA 4500 - Internship in Data Science
1 to 3 credit hours
Prerequisites: Data Science major; approval of program director; a plan of activities with the associated employer prior to registration. Practical experience in a specific area of data science. Pass/Fail. May be repeated for a maximum of 6 credit hours; only 3 credit hours will count in the major.
- ECON 2420 - Principles of Economics, Microeconomics 3 credit hours
ECON 2420 - Principles of Economics, Microeconomics
3 credit hours
As an aid to understanding modern economic society: economic concepts of consumer and firm behavior; the pricing of goods, services, and productive factors; international topics; and an overview of the American economy.
- ECON 4620 - Econometrics and Forecasting 3 credit hours
ECON 4620 - Econometrics and Forecasting
3 credit hours
Prerequisites: ECON 2410, ECON 2420; MATH 1810 or MATH 1910; junior standing, and admission into the College of Business. The application of statistical methods to economic problems; covers statistical inference, regression analysis in economics and finance, and an introduction to econometrics. Emphasis on applications to actual economic data and includes use of econometric software.
- ACSI 4600 - Problems in Actuarial Science 1 to 6 credit hours OR
ACSI 4600 - Problems in Actuarial Science
1 to 6 credit hours
Prerequisites: Senior standing and consent of instructor. Students wishing to enroll must submit a written course/topic proposal to the department prior to the semester in which ACSI 4600 is taken. Proposal must be approved prior to taking the course. At the conclusion, each enrollee must submit a written report to the department.
- STAT 4600 - Problems in Statistics 1 to 6 credit hours
STAT 4600 - Problems in Statistics
1 to 6 credit hours
Prerequisites: Senior standing and consent of instructor. Students wishing to enroll must submit a written course/topic proposal to the department prior to the semester in which STAT 4600 is taken. Proposal must be approved prior to taking the course. At the conclusion, each enrollee must submit a written report to the department.
- Courses from cognate areas not selected above (in consultation with advisor)
Supporting Courses (7 hours)
- ECON 2410 - Principles of Economics, Macroeconomics 3 credit hours (may be counted in General Education)
ECON 2410 - Principles of Economics, Macroeconomics
3 credit hours
As an aid to understanding modern economic society: economic concepts of national income and its fluctuations, inflation, unemployment, role of the banking system, monetary and fiscal policies, and international topics.
- MATH 1910 - Calculus I 4 credit hours (may be counted in General Education)
MATH 1910 - Calculus I
4 credit hours
Prerequisite: MATH 1730 with a grade of C or better or Math ACT of 26 or better or satisfactory score on Calculus placement test. An introduction to calculus with an emphasis on analysis of functions, multidisciplinary applications of calculus, and theoretical understanding of differentiation and integration. Topics include the definition of the derivative, differentiation techniques, and applications of the derivative. Calculus topics related to trigonometric, exponential, and logarithmic functions also included. Course concludes with the fundamental theorem of calculus; the definition of antidifferentiation and the definite integral; basic applications of integrations; and introductory techniques of integration. Graphing calculator required. TBR Common Course: MATH 1910
Electives/Minor (15-21 hours)
- Minor strongly recommended
Curriculum: Data Science
Curricular listings include General Education requirements in Communication, History, Humanities and/or Fine Arts, Mathematics, Natural Sciences, and Social/Behavioral Sciences categories.
Students should consult their advisors each semester to plan their schedules.
Freshman Fall
- DATA 1500 - Introduction to Data Science 3 credit hours
DATA 1500 - Introduction to Data Science
3 credit hours
(Same as BIA 1500.) Introduces basic principles and tools as well as its general mindset in data science. Concepts on how to solve a problem with data include business and data understanding, data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
- ENGL 1010 - Expository Writing 3 credit hours (Comm)
ENGL 1010 - Expository Writing
3 credit hours
The first General Education English course. Emphasis on learning to adapt composing processes to a variety of expository and analytic writing assignments. Minimum grade of C- required to meet degree requirements.
- MATH 1910 - Calculus I 4 credit hours (Math)
MATH 1910 - Calculus I
4 credit hours
Prerequisite: MATH 1730 with a grade of C or better or Math ACT of 26 or better or satisfactory score on Calculus placement test. An introduction to calculus with an emphasis on analysis of functions, multidisciplinary applications of calculus, and theoretical understanding of differentiation and integration. Topics include the definition of the derivative, differentiation techniques, and applications of the derivative. Calculus topics related to trigonometric, exponential, and logarithmic functions also included. Course concludes with the fundamental theorem of calculus; the definition of antidifferentiation and the definite integral; basic applications of integrations; and introductory techniques of integration. Graphing calculator required. TBR Common Course: MATH 1910
- Humanities and/or Fine Arts 3 credit hours
- HIST 2010 - Survey of United States History I 3 credit hours OR
HIST 2010 - Survey of United States History I
3 credit hours
Survey of the political, economic, social, cultural, and diplomatic phases of American life in its regional, national, and international aspects. Discusses the era from the beginning to 1877. May be used to satisfy one part of the General Education History requirement. HIST 2010 is NOT a prerequisite for HIST 2020. TBR Common Course: HIST 2010
- HIST 2020 - Survey of United States History II 3 credit hours OR
HIST 2020 - Survey of United States History II
3 credit hours
Survey of the political, economic, social, cultural, and diplomatic phases of American life in its regional, national, and international aspects. Discusses the era from 1877 to the present. May be used to satisfy one part of the the General Education History requirement. HIST 2010 is NOT a prerequisite for HIST 2020. TBR Common Course: HIST 2020
- HIST 2030 - Tennessee History 3 credit hours OR
HIST 2030 - Tennessee History
3 credit hours
The role of the state in the development of the nation. May be used to satisfy one part of the General Education History requirement. TBR Common Course: HIST 2030
- HIST 2040 - Survey African American History I 3 credit hours OR
HIST 2040 - Survey African American History I
3 credit hours
The role of African Americans in establishing and shaping the American nation. Covers their historical development and contributions to American art, music, literature, and religion. May be used to satisfy one part of the General Education History requirement.
NOTE: HIST 2040 is not a prerequisite for HIST 2050.
- HIST 2050 - Survey African American History II 3 credit hours
HIST 2050 - Survey African American History II
3 credit hours
The role of African Americans in shaping the American nation and creating a twentieth-century racial identity. Covers their historical development and examines their contributions to American art, music, literature, and religion. May be used to satisfy one part of the General Education History requirement.
Subtotal: 16 Hours
Freshman Spring
- ENGL 1020 - Research and Argumentative Writing 3 credit hours (Comm)
ENGL 1020 - Research and Argumentative Writing
3 credit hours
Prerequisite: ENGL 1010. The second General Education English course. Emphasis on analytic and argumentative writing and on locating, organizing, and using library resource materials in the writing. Minimum grade of C- required to meet degree requirements.
- ECON 2410 - Principles of Economics, Macroeconomics 3 credit hours (Soc/Beh Sci)
ECON 2410 - Principles of Economics, Macroeconomics
3 credit hours
As an aid to understanding modern economic society: economic concepts of national income and its fluctuations, inflation, unemployment, role of the banking system, monetary and fiscal policies, and international topics.
- Humanities and/or Fine Arts 3 credit hours
- MATH 1530 - Applied Statistics 3 credit hours OR
MATH 1530 - Applied Statistics
3 credit hours
Prerequisites: Two years of high school algebra and a Math Enhanced ACT 19 or greater or equivalent. Descriptive statistics, probability, and statistical inference. The inference unit covers means, proportions, and variances for one and two samples, and topics from one-way ANOVA, regression and correlation analysis, chi-square analysis, and nonparametrics. TBR Common Course: MATH 1530
- MATH 2050 - Probability and Statistics 3 credit hours OR
MATH 2050 - Probability and Statistics
3 credit hours
Prerequisite: MATH 1810 or MATH 1910. Data analysis, probability, and statistical inference. The inference material covers means, proportions, and variances for one and two samples, one-way ANOVA, regression and correlation, and chi-square analysis. TBR Common Course: MATH 2050
- BIA 2610 - Statistical Methods 3 credit hours
BIA 2610 - Statistical Methods
3 credit hours
The application of collecting, summarizing, and analyzing data to make business decisions. Topics include measures of central tendency, variation, probability theory, point and interval estimation, correlation and regression. Computer applications emphasized.
- HIST 2010 - Survey of United States History I 3 credit hours OR
HIST 2010 - Survey of United States History I
3 credit hours
Survey of the political, economic, social, cultural, and diplomatic phases of American life in its regional, national, and international aspects. Discusses the era from the beginning to 1877. May be used to satisfy one part of the General Education History requirement. HIST 2010 is NOT a prerequisite for HIST 2020. TBR Common Course: HIST 2010
- HIST 2020 - Survey of United States History II 3 credit hours OR
HIST 2020 - Survey of United States History II
3 credit hours
Survey of the political, economic, social, cultural, and diplomatic phases of American life in its regional, national, and international aspects. Discusses the era from 1877 to the present. May be used to satisfy one part of the the General Education History requirement. HIST 2010 is NOT a prerequisite for HIST 2020. TBR Common Course: HIST 2020
- HIST 2030 - Tennessee History 3 credit hours OR
HIST 2030 - Tennessee History
3 credit hours
The role of the state in the development of the nation. May be used to satisfy one part of the General Education History requirement. TBR Common Course: HIST 2030
- HIST 2040 - Survey African American History I 3 credit hours OR
HIST 2040 - Survey African American History I
3 credit hours
The role of African Americans in establishing and shaping the American nation. Covers their historical development and contributions to American art, music, literature, and religion. May be used to satisfy one part of the General Education History requirement.
NOTE: HIST 2040 is not a prerequisite for HIST 2050.
- HIST 2050 - Survey African American History II 3 credit hours
HIST 2050 - Survey African American History II
3 credit hours
The role of African Americans in shaping the American nation and creating a twentieth-century racial identity. Covers their historical development and examines their contributions to American art, music, literature, and religion. May be used to satisfy one part of the General Education History requirement.
Subtotal: 15 Hours
Sophomore Fall
- ENGL 2020 - Themes in Literature and Culture 3 credit hours (Hum/FA) OR
ENGL 2020 - Themes in Literature and Culture
3 credit hours
Prerequisites: ENGL 1010 and ENGL 1020. Traces a specific theme or idea through a number of literary texts that reflect different historical and cultural contexts. Subject will vary.
- ENGL 2030 - The Experience of Literature 3 credit hours (Hum/FA) OR
ENGL 2030 - The Experience of Literature
3 credit hours
Prerequisites: ENGL 1010 and ENGL 1020. The reading of a variety of literary types which illuminate themes and experiences common to human existence.
- HUM 2610 - World Literatures 3 credit hours (Hum/FA)
HUM 2610 - World Literatures
3 credit hours
Prerequisites: ENGL 1010 and ENGL 1020. Representative works of French, German, and Hispanic authors in English translation. No foreign-language proficiency required. Carries General Education credit.
- CSCI 1170 - Computer Science I 4 credit hours
CSCI 1170 - Computer Science I
4 credit hours
Prerequisite: MATH 1730 or MATH 1810 with a grade of C or better or Math ACT of 26 or better or Calculus placement test score of 73 or better. The first of a two-semester sequence using a high-level language; language constructs and simple data structures such as arrays and strings. Emphasis on problem solving using the language and principles of structured software development. Three lecture hours and two laboratory hour.
- MATH 2530 - Applied Statistics II 3 credit hours
MATH 2530 - Applied Statistics II
3 credit hours
Prerequisite: MATH 1530 or MATH 2050 or equivalent. Explores the application of the following statistical methods: analysis of variance, simple and multiple regression models, categorical data analysis, and nonparametric methods. Three hours lecture per week.
- Natural Sciences 4 credit hours
- Social/Behavioral Sciences 3 credit hours
Subtotal: 17 Hours
Sophomore Spring
- COMM 2200 - Fundamentals of Communication 3 credit hours
COMM 2200 - Fundamentals of Communication
3 credit hours
Introduces principles and processes of effective public oral communication including researching, critical thinking, organizing, presenting, listening, and using appropriate language. Counts as part of the General Education Communication requirement. TBR Common Course: COMM 2025
- CSCI 2170 - Computer Science II 4 credit hours
CSCI 2170 - Computer Science II
4 credit hours
Prerequisites: CSCI 1170 (or equivalent) with a grade of C or better and MATH 1730 or MATH 1810 with a grade of C or better or Math ACT of 26 or better or Calculus placement test score of 73 or better. A continuation of CSCI 1170. Topics include introductory object-oriented programming techniques, software engineering principles, records, recursion, pointers, stacks and queues, linked lists, trees, and sorting and searching. Three lecture hours and two laboratory hours.
- MATH 2110 - Data Analysis 1 credit hour
MATH 2110 - Data Analysis
1 credit hour
Prerequisite or corequisite: MATH 1530 or MATH 2050 or equivalent. Using computer software for graphing and analysis of scientific and statistical data.
- Elective/minor 3 credit hours
- Natural Sciences 4 credit hours
Subtotal: 15 Hours
Junior Fall
- BIA 3620 - Introduction to Business Analytics 3 credit hours
BIA 3620 - Introduction to Business Analytics
3 credit hours
Prerequisites: BIA 2610 or MATH 1530, junior standing. Introduces the concepts and application of data analytics in business. Spreadsheet software and associated analytic tools utilized to visualize, model, and analyze business data using a hands-on-approach.
- DATA 3500 - Data Cleansing and Feature Engineering 3 credit hours
DATA 3500 - Data Cleansing and Feature Engineering
3 credit hours
Prerequisite: CSCI 1170. Techniques and applications used to collect and integrate data, inspect the data for errors, visualize and summarize the data, clean the data, and prepare the data for modeling for various data types.
- DS cognate 3 credit hours
- Elective/minor 6 credit hours
Subtotal: 15 Hours
Junior Spring
- DATA 3550 - Applied Predictive Modeling 3 credit hours
DATA 3550 - Applied Predictive Modeling
3 credit hours
(Same as STAT 3550.) Prerequisite: CSCI 1170. An overview of the modeling process used in data science. Covers the ethics involved in data science, data preprocessing, regression models, classification models, and presenting the model.
- Elective/minor 6 credit hours
- DS cognate 3 credit hours
- DS elective 3 credit hours
Subtotal: 15 Hours
Senior Fall
- INFS 4790 - Database Design and Development 3 credit hours
INFS 4790 - Database Design and Development
3 credit hours
Prerequisites: INFS 2600, ISA major, junior standing, and admission into the College of Business. Fundamental concepts: conventional data systems, integrated management information systems, database structure systems, data integration, complex file structure, online access systems. Emphasis on total integrated information systems database and database management languages.
- DS cognate 3 credit hours
- DS elective 6 credit hours
- Elective/minor 3 credit hours
Subtotal: 15 Hours
Senior Spring
- DATA 4950 - Data Science Capstone 3 credit hours
DATA 4950 - Data Science Capstone
3 credit hours
Prerequisites: Senior standing; Data Science major; DATA 3500 and DATA 3550. A project-based course that will utilize data science skills to prepare, display, model, analyze, and present data to solve a real-world problem.
- DS cognate 3 credit hours
- DS elective 3 credit hours
- Elective/minor 3 credit hours
Subtotal: 12 Hours
Our adjunct faculty bring outstanding professional experience to our programs. Many are industry leaders with decorated careers and honors. Importantly, they are innovative educators who offer hands-on learning to our students to prepare them to enter and thrive in a dynamic, and oftentimes emerging, industry and professional world. They inspire, instruct, and challenge our students toward academic and professional success.
DATA 1500 - Introduction to Data Science
3 credit hours
3 credit hours
(Same as BIA 1500.) Introduces basic principles and tools as well as its general mindset in data science. Concepts on how to solve a problem with data include business and data understanding, data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
DATA 3500 - Data Cleansing and Feature Engineering
3 credit hours
3 credit hours
Prerequisite: CSCI 1170. Techniques and applications used to collect and integrate data, inspect the data for errors, visualize and summarize the data, clean the data, and prepare the data for modeling for various data types.
DATA 3550 - Applied Predictive Modeling
3 credit hours
3 credit hours
(Same as STAT 3550.) Prerequisite: CSCI 1170. An overview of the modeling process used in data science. Covers the ethics involved in data science, data preprocessing, regression models, classification models, and presenting the model.
DATA 4500 - Internship in Data Science
1 to 3 credit hours
1 to 3 credit hours
Prerequisites: Data Science major; approval of program director; a plan of activities with the associated employer prior to registration. Practical experience in a specific area of data science. Pass/Fail. May be repeated for a maximum of 6 credit hours; only 3 credit hours will count in the major.
DATA 4950 - Data Science Capstone
3 credit hours
3 credit hours
Prerequisites: Senior standing; Data Science major; DATA 3500 and DATA 3550. A project-based course that will utilize data science skills to prepare, display, model, analyze, and present data to solve a real-world problem.
Data Science Institute
The Data Science Institute develops people for the data world. This is accomplished by facilitating interdisciplinary research, industry and government partnerships, and community outreach which puts MTSU at the forefront of data science regionally and nationally in terms of education and research. The Data Science Institute also looks to develop people through doing by offering opportunities for projects, events (data dives), and training.
Data Dives
To learn Data Science, you must DO Data Science. The Data Science Institute offers hackathon style Data dives that allow students to work together to solve real world problems using data. The Data dives have ranged from 16 hours over two days to 24 hour overnight events and have analyzed data from several non-profits such as Second Harvest, Special Kids, Inc., and Murfreesboro Police Department.
Alex Antonison from Stratasan (far left), works through a problem for a Data Dive
at MTSU to help Second Harvest Food Bank with warehouse optimization.
Online or Hybrid Programs at a Glance
This program is available .
For More Information or Explore Your Options
Contact your department / program coordinator or advisor for more details about the program OR work one-on-one with your advisor to explore your options.
The Online Advantage
With over 25 years of experience in online teaching and learning, MTSU Online offers students access to innovative, high-quality programs. Designed with students in mind, our courses allow maximum flexibility for those unable to participate in person.
Resources and services for online students are available from MTSU Online or contact us at distance@mtsu.edu.
