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Data Science

Bachelor of Science Degree

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Learn more about our program...

A degree in DATA SCIENCE from an institution based in the liberal education tradition offers a breadth of knowledge needed to approach and solve complex problems. This emerging field did not exist 10 years ago and is evolving rapidly with the exponential increase of data from online, social media and the expanding Web of Things. It is an area in which many disciplines and backgrounds participate. A critical need exists for data scientists who can collect and identify key data, analyze that data using appropriate methods and draw sound conclusions which inform the decision making process. Students receive experiential learning paired with coursework that integrates data analysis and decision making throughout the curriculum.

Offerings
Major, Minor
Field of Study
Science & Math
Format
Lamoni Campus
Curriculum
  • 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

Exceptional Faculty
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Career Growth Ahead

A data science major from Graceland University can help to prepare you for these careers:

  • Data scientist/analyst 
  • Computer and information research scientist 
  • Information security analyst 
  • Computer systems analyst 
  • Statistician 
  • Data engineer/architect 
  • Business intelligence (BI) developer 
  • Infrastructure architect 

       …and many other exciting fields. 

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Tuition & Aid

An education from Graceland is one of the greatest investments you can make but we understand that cost is often a concern. See our direct undergraduate tuition costs, room and board and other student fees and use our tuition calculator to get an estimate of what your cost will be. Find out how what a sample Graceland financial aid award package looks like.

ExpenseAnnual CostSemester Cost
Tuition$31,250$15,625
Room (based on a two-person room)$3,770$1,885
Board$6,040$3,020
Activity Fee $370$185
University Technology Fee$300$150
TOTAL DIRECT COSTS$41,730$20,865

 “Graceland’s data science program is built on the concept of converting data to information so that key questions can be answered and advancements made. In the 21st Century, data skills like those developed in Graceland’s program will be required for successful careers in nearly all areas.” 

 Jeff Draves, PhD Professor of Chemistry Director of Data Science & Analytics 

Professor Jeff Draves

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The "4+1" Program

Master of Science Degree in Data Science and Analytics – The “4+1” Program

Graceland undergraduates majoring in Data Science and those undergraduates that will have completed an Analytics Track by graduation may take up to 15 semester hours in the MS program and count these as credit toward their BS degree and credit toward their MS degree. Note that all applicants must also have completed the course requirements, GPA requirement, and letter of recommendation requirement of the twenty-two month program. Completion of DSCI5300 Introduction to Data Science, DSCI5320 Practical Applications of Data Science, and DSCI5330 Extracting and Transforming Data as undergraduates will allow Graceland students the opportunity to finish the Master Degree within 16 months or less after graduation from the BS program.

 

Why Graceland

 • Graceland’s essential education encourages critical thinking and well-balanced learning that contributes to creative problem solving. 

• Data science majors engage in a curriculum that integrates data skills including statistics, coding and content knowledge. 

• The data science program is a great choice for anyone seeking careers related to data science and analysis, which can be found in government, areas of industry and academia. 

Find out more!