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  • BS Degree — Data Science Major

    In addition to the essential education requirements, majors in Data Science must complete 45 semester hours of coursework as described below:

    Courses Offered
    • CSIT1100Principles of Computing
      CSIT1100 Principles of Computing - 3 s.h.

      An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

    • CSIT1200Data Structures
      CSIT1200 Data Structures - 3 s.h.

      Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

    • CSIT3300Database Concepts and SQL
      CSIT3300 Database Concepts and SQL - 3 s.h.

      A study of the concepts and structures required to implement a database system including the logical design and physical organization of the database. Emphasis is given to the design and development of database systems that includes understanding and applying entity-relationship models. Implementation of a database using SQL on a database system is included. Prerequisite: CSIT1100.

    • CSIT4200Machine Learning
      CSIT4200 Machine Learning - 3 s.h.

      A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2520, MATH3200, CSIT1200.

    • CSIT4300Cluster Algorithms
      CSIT4300 Cluster Algorithms - 3 s.h.

      Basic concepts of cluster analysis and algorithms are introduced. Methods for clustering validation and evaluation of clustering quality. Prerequisites: CSIT4200 (Machine Learning).

    • DSCI1500Beginning Data Science and Data Analytics
      DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

      Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    • MATH1380Introduction to Statistics
      MATH1380 Introduction to Statistics - 3 s.h.

      Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. Elementary ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. Goal 3A, ELO6 Math

    • MATH1510Calculus I
      MATH1510 Calculus I - 4 s.h.

      Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. Goal 3A, ELO6 Math

    • MATH1520Calculus II
      MATH1520 Calculus II - 4 s.h.

      Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

    • MATH2350Discrete Mathematics
      MATH2350 Discrete Mathematics - 3 s.h.

      A survey of topics in discrete mathematics focusing on introductory logic, methods of mathematical proof, set theory, determinants and matrices, combinatorics, and graph theory. Prerequisite: Instructor approval for non-CSIT/MATH majors, 2 years high school algebra or MATH1280. Goal 3A, ELO6 Math

    • MATH2510Calculus III
      MATH2510 Calculus III - 4 s.h.

      Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520. Goal 3A

    • MATH3200Probability and Stochastic Processes
      MATH3200 Probability and Stochastic Processes - 3 s.h.

      Introduction to probability, classical probability models and processes, random variables, conditional probability, Markov Chains, and application. Prerequisite: MATH1520 and MATH2350. Goal 3A +This course is only offered every other year.

    • MATH3340Linear Algebra
      MATH3340 Linear Algebra - 3 s.h.

      Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. Goal 3A. +This course is only offered every other year.

    • MATH4380Advanced Statistics
      MATH4380 Advanced Statistics - 3 s.h.

      A study of linear and generalized regression; random-effects models; methods for categorical data; survival analysis; and nonparametric methods, modeling. exploratory data analysis; modern nonparametric regression. Prerequisite: MATH1380, MATH2350.

    CSIT1100Principles of Computing CSIT1200Data Structures CSIT3300Database Concepts and SQL CSIT4200Machine Learning CSIT4300Cluster Algorithms DSCI1500Beginning Data Science and Data Analytics MATH1380Introduction to Statistics MATH1510Calculus I MATH1520Calculus II MATH2350Discrete Mathematics MATH2510Calculus III MATH3200Probability and Stochastic Processes MATH3340Linear Algebra MATH4380Advanced Statistics
    Course Descriptions
    CSIT1100 Principles of Computing - 3 s.h.

    An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

    CSIT1200 Data Structures - 3 s.h.

    Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

    CSIT3300 Database Concepts and SQL - 3 s.h.

    A study of the concepts and structures required to implement a database system including the logical design and physical organization of the database. Emphasis is given to the design and development of database systems that includes understanding and applying entity-relationship models. Implementation of a database using SQL on a database system is included. Prerequisite: CSIT1100.

    CSIT4200 Machine Learning - 3 s.h.

    A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2520, MATH3200, CSIT1200.

    CSIT4300 Cluster Algorithms - 3 s.h.

    Basic concepts of cluster analysis and algorithms are introduced. Methods for clustering validation and evaluation of clustering quality. Prerequisites: CSIT4200 (Machine Learning).

    DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

    Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    MATH1380 Introduction to Statistics - 3 s.h.

    Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. Elementary ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. Goal 3A, ELO6 Math

    MATH1510 Calculus I - 4 s.h.

    Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. Goal 3A, ELO6 Math

    MATH1520 Calculus II - 4 s.h.

    Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

    MATH2350 Discrete Mathematics - 3 s.h.

    A survey of topics in discrete mathematics focusing on introductory logic, methods of mathematical proof, set theory, determinants and matrices, combinatorics, and graph theory. Prerequisite: Instructor approval for non-CSIT/MATH majors, 2 years high school algebra or MATH1280. Goal 3A, ELO6 Math

    MATH2510 Calculus III - 4 s.h.

    Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520. Goal 3A

    MATH3200 Probability and Stochastic Processes - 3 s.h.

    Introduction to probability, classical probability models and processes, random variables, conditional probability, Markov Chains, and application. Prerequisite: MATH1520 and MATH2350. Goal 3A +This course is only offered every other year.

    MATH3340 Linear Algebra - 3 s.h.

    Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. Goal 3A. +This course is only offered every other year.

    MATH4380 Advanced Statistics - 3 s.h.

    A study of linear and generalized regression; random-effects models; methods for categorical data; survival analysis; and nonparametric methods, modeling. exploratory data analysis; modern nonparametric regression. Prerequisite: MATH1380, MATH2350.

  • Data Science Minor

    A minor in Data Science requires 20 semester hours as described below:

    Courses Offered
    • CSIT1100Principles of Computing
      CSIT1100 Principles of Computing - 3 s.h.

      An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

    • CSIT1200Data Structures
      CSIT1200 Data Structures - 3 s.h.

      Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

    • DSCI1500Beginning Data Science and Data Analytics
      DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

      Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    • MATH1380Introduction to Statistics
      MATH1380 Introduction to Statistics - 3 s.h.

      Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. Elementary ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. Goal 3A, ELO6 Math

    • MATH1510Calculus I
      MATH1510 Calculus I - 4 s.h.

      Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. Goal 3A, ELO6 Math

    • MATH1520Calculus II
      MATH1520 Calculus II - 4 s.h.

      Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

    CSIT1100Principles of Computing CSIT1200Data Structures DSCI1500Beginning Data Science and Data Analytics MATH1380Introduction to Statistics MATH1510Calculus I MATH1520Calculus II
    Course Descriptions
    CSIT1100 Principles of Computing - 3 s.h.

    An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

    CSIT1200 Data Structures - 3 s.h.

    Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

    DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

    Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    MATH1380 Introduction to Statistics - 3 s.h.

    Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. Elementary ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. Goal 3A, ELO6 Math

    MATH1510 Calculus I - 4 s.h.

    Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. Goal 3A, ELO6 Math

    MATH1520 Calculus II - 4 s.h.

    Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

  • Course list - Data Science
    Courses Offered
    • DSCI1500Beginning Data Science and Data Analytics
      DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

      Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    • DSCI4300Introduction to Data Science
      DSCI4300 Introduction to Data Science - 3 s.h.

      An introduction to the methods of data science through a combination of computational exploration, visualization, and theory. Students will learn scientific computing basics, topics in numerical linear algebra, mathematical probability, statistics, and social and political issues raised by data science. Prerequisites: Prior courses in statistics, calculus and basic programing. This course accompanies the DSCI5300 course for students who have been admitted to the 4+1 program. Students in the 4+1 program must also sign up for DSCI4301 and complete all graduate level assignments.

    • DSCI4330Extracting and Transforming Data
      DSCI4330 Extracting and Transforming Data - 3 s.h.

      Students will learn skills of data acquisition, methods of data cleaning, imputing data, data storage and other important issues required to producing useable data sets. Codebooks, data standards, and markdown files will be introduced as well as the concept of the data lake. Prerequisites: DSCI4300. This course accompanies the DSCI5330 course for students who have been admitted to the 4+1 program. Students in the 4+1 program must also sign up for DSCI4331 and complete all graduate level assignments.

    DSCI1500Beginning Data Science and Data Analytics DSCI4300Introduction to Data Science DSCI4330Extracting and Transforming Data
    Course Descriptions
    DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.

    Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

    DSCI4300 Introduction to Data Science - 3 s.h.

    An introduction to the methods of data science through a combination of computational exploration, visualization, and theory. Students will learn scientific computing basics, topics in numerical linear algebra, mathematical probability, statistics, and social and political issues raised by data science. Prerequisites: Prior courses in statistics, calculus and basic programing. This course accompanies the DSCI5300 course for students who have been admitted to the 4+1 program. Students in the 4+1 program must also sign up for DSCI4301 and complete all graduate level assignments.

    DSCI4330 Extracting and Transforming Data - 3 s.h.

    Students will learn skills of data acquisition, methods of data cleaning, imputing data, data storage and other important issues required to producing useable data sets. Codebooks, data standards, and markdown files will be introduced as well as the concept of the data lake. Prerequisites: DSCI4300. This course accompanies the DSCI5330 course for students who have been admitted to the 4+1 program. Students in the 4+1 program must also sign up for DSCI4331 and complete all graduate level assignments.