With a data science degree from Graceland University, you’ll use statistics, scientific computing, and other scientific processes and methods to understand data and find answers in a digital world.
A data science degree from an institution based on 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. Industries that offer data science careers include healthcare, finance, marketing, and technology, among others.
A critical need exists for data scientists who can collect and identify key data, analyze that data using appropriate methods, and draw sound conclusions that inform the decision-making process. Students receive experiential learning paired with data science courses that integrate data analysis and decision-making throughout the curriculum.
In addition to the general education requirements, majors in Data Science must complete 35 semester hours of coursework as described below:
*General Education Requirement
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
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.
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.
A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2510, CSIT1200. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5370 Machine Learning.)
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.
Exploratory data analysis is introduced along with fundamental considerations for data analysis on real data sets. Classical models and techniques for classification are included. Methods of data visualization are introduced. Prerequisites: CSIT4200 (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5320 Practical Application of Data Science.)
Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520.
Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. +This course is only offered every other year.
Introduction to probability, classical probability models and processes, random variables, conditional probability, bivariate distributions and their development, goodness of fit tests, and other nonparametric methods. Prerequisite: MATH1520 and MATH2350. +This course is only offered every other year. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5340 Probability and Statistical Inference.)
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
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.
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.
A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2510, CSIT1200. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5370 Machine Learning.)
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.
Exploratory data analysis is introduced along with fundamental considerations for data analysis on real data sets. Classical models and techniques for classification are included. Methods of data visualization are introduced. Prerequisites: CSIT4200 (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5320 Practical Application of Data Science.)
Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520.
Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. +This course is only offered every other year.
Introduction to probability, classical probability models and processes, random variables, conditional probability, bivariate distributions and their development, goodness of fit tests, and other nonparametric methods. Prerequisite: MATH1520 and MATH2350. +This course is only offered every other year. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5340 Probability and Statistical Inference.)
A minor in Data Science requires 20 semester hours as described below:
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
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.
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.
Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510.
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
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.
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.
Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
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. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.
Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510.
Data analytics certificates are designed to equip students with:
Data analytics certificates are broken down into three levels:
Students can select from the following domain specific certificates:
A minor in Data Science requires 20 semester hours as described below:
Data science is an interdisciplinary field, and Graceland’s data science degree program involves varied courses and instruction to develop well-rounded students ready for the industry. Students will go on to work in careers in the healthcare industry, finance, marketing, and more. A data science major from Graceland University can help to prepare you for these careers:
…and many other exciting fields.
99% of students on the Lamoni campus receive financial aid.
As you consider college, you want a simple, easy-to-understand formula designed to ensure that the one-of-a-kind Graceland Experience is within reach for your family. Undergraduate tuition includes our unique Transformational Leadership major, and we offer generous financial aid and scholarships to all of our students, making Graceland as affordable, if not moreso, than most public universities.
Exclusively for students majoring in Allied Health, Biology, Chemistry, Computer Science and Information Technology, and Data Science, the Resch Scholars Program rewards high-performing science students through a combination of enhanced learning opportunities and the Resch Scholars Program scholarship. The scholarship is for a variable amount up to full tuition and is renewable for up to four years.
To be eligible, students must also hold a 3.0+ GPA (3.5+ GPA for full tuition) and Graceland must receive a completed FAFSA by January 1, 2024. Be sure to use Graceland’s FAFSA code 001866. Other eligibility and renewal requirements can be found on the Resch Scholars website at the link below.
Graceland undergraduates with a Data Science degree enrolled and our data science four-year plan and those undergraduates who will have completed an Analytics Track by graduation may take up to 15 semester hours in the MS program and count these as credits 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.
Graceland’s essential education encourages critical thinking and well-balanced learning that contributes to creative problem-solving. This type of course design is great for data science students who want to establish themselves as well-rounded employees. Data science majors engage in a curriculum that integrates data skills including statistics, coding, and content knowledge. The data science degree 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.
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