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Data Science, B.S.

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Creating Data Driven Problem Solvers

Data science continues to be in high demand and data practitioners are needed in every industry. A degree in Data Science provides a unique interdisciplinary approach to education that includes courses and faculty from several departments, including Computer Science, Mathematics, Information Systems & Analytics, and Economics and Finance. This degree leverages the strengths of MTSU by bringing the best of all of these areas together to create future career ready professionals.

Students will take courses in programming, statistics, analytics, database, and machine learning as well as selecting a concentration in either Inferential Thinking, Business Intelligence, or Advanced Machine Learning.  Not only will students be taking innovative and interdisciplinary courses, but they will also be able to put those skills to good use through ‘real-world’ projects through the Data Science Institute and events such as Data Dives and Hackathons.  A degree that truly makes you career ready through courses and experiences to create data driven problem solvers. 


What We're Doing

Students working in MTSU’s Data Science Institute

Data Science students dig deep into data for Hytch traffic rewards app

Students working in MTSU’s Data Science Institute dug deep into “big data” provided by Midstate social impact technology company Hytch to see how users of its app-based rewards program are affecting traffic throughout the region. The Nashville-based company hired the MTSU institute to study data from the Hytch Rewards app and offer the company insights on how commuters are using the service, which seeks to reduce traffic congestion and pollution through ride sharing, carpooling or use of public transit. “With so many miles shared and payments distributed by Hytch — analyzing that much data takes time and resources. The data might prove how inexpensive it is to modify behavior and get more value out of our existing road network,” said MTSU Data Science Institute director Charlie Apigian. The Hytch project involved four students — one undergraduate, two master’s students and one doctoral candidate — as well as two alumni mentors. The team was also interdisciplinary, ranging from information systems to computational science majors, with students and mentors earning a small stipend for their work.

Dr. Charlie Apigian with award

Professor, student receive Nashville Technology Council awards

Middle Tennessee State University represented well at the 2020 Nashville Technology Council Awards, bringing home three of the major awards for the evening. Data Science professor, Dr. Charles Apigian was named “Data Scientist of the Year,” while graduate student Luis Lange was named “Technology Student of the Year,” and the MTSU Women of STEM Center was awarded the “Diversity and Inclusion Initiative of the Year.” These are incredible accomplishments that prove that MTSU’s efforts in promoting education in data science are preparing students to make valuable contributions in the rapidly growing Nashville technology community. The Nashville Technology Council has worked as a hub for bringing technology companies together and recognizing industry leaders. As a $7.5 billion-dollar industry that continues to flourish, the demand for information technology jobs will continue to grow, and MTSU has industry-leading resources, expert faculty, and driven students that will make an impact for years to come.


Related Media

  • Data Science Institute to tackle Mars, autonomous vehicles: "Out of the Blue," July 2018

    Data Science Institute to tackle Mars, autonomous vehicles: "Out of the Blue," July 2018

  • MTSU Campus Tour

    MTSU Campus Tour

  • Out of the Blue: Charlie Apigian, Lisa Green Discuss 'Data Science Initiative'

    Out of the Blue: Charlie Apigian, Lisa Green Discuss 'Data Science Initiative'

  • 'Data Science Initiative' Announced at Nashville Technology Council

    'Data Science Initiative' Announced at Nashville Technology Council

 
 
 

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
  • Hospital Corporation of America (HCA)
  • HPA Cognizant Technologies
  • Ingram Content Group
  • Nissan
  • State of Tennessee
  • Stratasan
  • The General
  • Tractor Supply
  • Trinisys
  • Vaco
  • Vanderbilt Medical

MTSU's offers a Bachelor of Science degree in Data Science with three tracks, 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.

Students can also choose to minor in Data Science.  The requirements include:

  • MATH 1530 or BIA 2610
  • CSCI 1170 Introduction to Computer Programming (prereq. sufficient background in Algebra and Trigonometry)
  • DATA 1500 Introduction in Data Science (no prereq)
  • DATA 3500 Data Cleansing (prereq: CSCI 1170)
  • DATA 3550 Predictive Modeling (prereq: CSCI 1170)

Graduate

For graduate students, a graduate certificate in Data Science is offered. This includes four courses (listed below) that are mostly online courses with Data Dives (hackathons) as in person experiential opportunities.  Each course is 7 weeks long and can be completed in two semesters.

  • DATA - 6300 - Data Understanding
  • DATA - 6310 - Data Exploration
  • DATA - 6320 - Predictive Modeling
  • DATA - 6330 - Model Optimization and Deployment

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.  To minor in Data Science, you are required to take 15 credit hours, which is listed below.

Required courses (5 required courses):

  • MATH 1530 or BIA 2610
  • CSCI 1170      Introduction to Computer Programming (prereq. sufficient background in Algebra and Trigonometry)
  • DATA 1500 Introduction in Data Science (no prereq)
  • DATA 3500 Data Cleansing (prereq: CSCI 1170)
  • DATA 3550 Predictive Modeling (prereq: CSCI 1170) 

For more information about the Data Science minor at MTSU, please contact Dr. Lisa Green, Program Director (Lisa.Green@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 understanding 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.

Majors courses require C or better.

Academic Map

Following is a printable, suggested four-year schedule of courses:

Data Science, B.S., Academic Map

Degree Requirements

General Education41 hoursMajor Requirements45 hoursData Science Electives12 hoursSupporting Courses7 hours*Electives15-21 hoursTOTAL120 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

    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

    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

    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

    (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

    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

    (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

    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

    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

    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  
    OROR  dotslash:OR title:OR 
    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  
    OROR  dotslash:OR title:OR 
    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

    Prerequisite: Calculus I. 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

    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

    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

    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

    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

  • BIA 3470 - Python for Business Applications  3 credit hours  
    OROR  dotslash:OR title:OR 
    OR 

    BIA 3470 - Python for Business Applications

    3 credit hours

    (Same as INFS 3470.) Prerequisite: ISA major and admission to College of Business. 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

    credit hours

    (Same as BIA 3470.) Prerequisites: Admission to College of Business and ISA major. 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

    Prerequisite: BIA 3620/BIA 3621 or an equivalent course; junior or senior standing. 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 4900 - 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

    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

    (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

    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

    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

    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

    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 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.

  • 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.

  • DATA 4500 - Internship in Data Science

    1 to 3 credit hours

    Prerequisites: Data Science; 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

    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

    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  
    OROR  dotslash:OR title:OR 
    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.

Supporting Courses (7 hours)

  • ECON 2410 - Principles of Economics, Macroeconomics  3 credit hours  
    (may be counted)(may be counted in General Education)  dotslash:(may be counted in General Education) title:(may be counted) 
    (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)(may be counted in General Education)  dotslash:(may be counted in General Education) title:(may be counted) 
    (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

    (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)(Comm)  dotslash:(Comm) title:(Comm) 
    (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 for credit.

  • MATH 1910 - Calculus I  4 credit hours  
    (Math)(Math)  dotslash:(Math) title:(Math) 
    (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  
    OROR  dotslash:OR title:OR 
    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. HIST 2010 discusses the era from the beginning to 1877. HIST 2020 discusses the era from 1877 to the present. These courses are prerequisite for all advanced courses in American history and satisfy 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  
    OROR  dotslash:OR title:OR 
    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. HIST 2010 discusses the era from the beginning to 1877. HIST 2020 discusses the era from 1877 to the present. These courses are prerequisite for all advanced courses in American history and satisfy 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

    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

Subtotal: 16 Hours

Freshman Spring

  • ENGL 1020 - Research and Argumentative Writing  3 credit hours  
    (Comm)(Comm)  dotslash:(Comm) title:(Comm) 
    (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 for credit.

  • ECON 2410 - Principles of Economics, Macroeconomics  3 credit hours  
    (Soc/Beh Sci)(Soc/Beh Sci)  dotslash:(Soc/Beh Sci) title:(Soc/Beh Sci) 
    (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  
    OROR  dotslash:OR title:OR 
    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  
    OROR  dotslash:OR title:OR 
    OR 

    MATH 2050 - Probability and Statistics

    3 credit hours

    Prerequisite: Calculus I. 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

    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  
    OROR  dotslash:OR title:OR 
    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. HIST 2010 discusses the era from the beginning to 1877. HIST 2020 discusses the era from 1877 to the present. These courses are prerequisite for all advanced courses in American history and satisfy 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  
    OROR  dotslash:OR title:OR 
    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. HIST 2010 discusses the era from the beginning to 1877. HIST 2020 discusses the era from 1877 to the present. These courses are prerequisite for all advanced courses in American history and satisfy 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

    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

Subtotal: 15 Hours

Sophomore Fall

  • ENGL 2020 - Themes in Literature and Culture  3 credit hours  
    (Hum/FA) OR(Hum/FA) OR  dotslash:(Hum/FA) OR title:(Hum/FA) OR 
    (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(Hum/FA) OR  dotslash:(Hum/FA) OR title:(Hum/FA) OR 
    (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 - Foreign Literature in Translation  3 credit hours  
    (Hum/FA)(Hum/FA)  dotslash:(Hum/FA) title:(Hum/FA) 
    (Hum/FA) 

    HUM 2610 - Foreign Literature in Translation

    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

    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

    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

    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

    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

    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

    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

    (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

    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

    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

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

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

(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

Prerequisites: Data Science; 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

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.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.

Contact Information

Dr. Lisa Green, Program Director
Lisa.Green@mtsu.edu
Room 325C Bldg KOM
615 898-5775
MTSU Box 0034

Who is My Advisor?

Kristen Janson (A-L)
Kristen.Janson@mtsu.edu
615-898-2276 | DSB 120

Hannah Trea (M-Z)
Hannah.Trea@mtsu.edu
615- 904-8307 | DSB 120

Mailing Address

Data Science Programs
c/o Dr. Lisa Green
Middle Tennessee State University
MTSU Box 34
1301 East Main Street
Murfreesboro, TN 37132

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