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 Master of Science in Data Science and Analytics 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, DSCI5320, and DSCI5330 as undergraduates will allow students the opportunity to finish the Master Degree within 16 months or less after graduation from the BS program.
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. 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. 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. 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. 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.
Students wishing to earn the Data Analytics for Accounting certificate must complete the following 18 credit hours with Graceland University.
An introduction to the study of accounting dealing with the preparation and analysis of the balance sheet, income statement, and related accounting records. Prerequisites: One MATH course.
The selection and analysis of accounting information for internal use by management. Prerequisite: ACCT2310.
An introduction to the study of auditing principles and standards. Provides a working knowledge of auditing procedures. Prerequisite: ACCT3360.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to the study of accounting dealing with the preparation and analysis of the balance sheet, income statement, and related accounting records. Prerequisites: One MATH course.
The selection and analysis of accounting information for internal use by management. Prerequisite: ACCT2310.
An introduction to the study of auditing principles and standards. Provides a working knowledge of auditing procedures. Prerequisite: ACCT3360.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students wishing to earn the Data Analytics for Agricultural Business certificate must complete the following 18 credit hours with Graceland University.
Exposure to accounting methods and taxation policies specific to agricultural producers and businesses. Prerequisite: ACCT2310 Financial Accounting.
Application of economics and financial resource allocation to agricultural businesses from producer to distributor to the end consumer. Content includes equity and credit practices for operations and for capital investments. Prerequisite: ECON1320 Microeconomics.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
Exposure to accounting methods and taxation policies specific to agricultural producers and businesses. Prerequisite: ACCT2310 Financial Accounting.
Application of economics and financial resource allocation to agricultural businesses from producer to distributor to the end consumer. Content includes equity and credit practices for operations and for capital investments. Prerequisite: ECON1320 Microeconomics.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
Students wishing to earn the Data Analytics for Business Management certificate must complete the following 18 credit hours with Graceland University.
Fundamentals of planning, organizing, directing, coordinating, and controlling business activity. Prerequisites: Junior standing.
Human aspects of business organization, as distinguished from economic and technical aspects, and how they influence efficiency, morale, and management practice. Offered Fall even years. +This course is only offered every other year.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
Fundamentals of planning, organizing, directing, coordinating, and controlling business activity. Prerequisites: Junior standing.
Human aspects of business organization, as distinguished from economic and technical aspects, and how they influence efficiency, morale, and management practice. Offered Fall even years. +This course is only offered every other year.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
Students wishing to earn the Data Analytics for Marketing certificate must complete the following 18 credit hours with Graceland University.
A focus on the practice of studying and managing marketing metrics data in order to enhance decision making for marketing efforts including calls-to-action (CTAs), blog posts, channel performance, and thought leadership pieces, and to identify opportunities for improvement and maximize marketing outcomes. Students will learn how marketing analytics professionals serve as liaisons between those who make marketing decisions and those who work with the data.
The aim of the course is to give the students a deeper understanding of marketing on a global basis. The students examine the international similarities and differences in marketing functions as related to the cultural, economic, political, social, and physical dimensions of the environment. This course is designed to provide students with an applied understanding of international marketing activities based on real-life examples.
This course is designed to equip students with the knowledge and skills to develop, implement, and evaluate strategic marketing initiatives in various business contexts. This course explores the fundamental principles, theories, and practices of strategic marketing, emphasizing its critical role in achieving a competitive advantage in today's dynamic and global business environment. Prerequisites: BUAD2330 and BUAD3240.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
A focus on the practice of studying and managing marketing metrics data in order to enhance decision making for marketing efforts including calls-to-action (CTAs), blog posts, channel performance, and thought leadership pieces, and to identify opportunities for improvement and maximize marketing outcomes. Students will learn how marketing analytics professionals serve as liaisons between those who make marketing decisions and those who work with the data.
The aim of the course is to give the students a deeper understanding of marketing on a global basis. The students examine the international similarities and differences in marketing functions as related to the cultural, economic, political, social, and physical dimensions of the environment. This course is designed to provide students with an applied understanding of international marketing activities based on real-life examples.
This course is designed to equip students with the knowledge and skills to develop, implement, and evaluate strategic marketing initiatives in various business contexts. This course explores the fundamental principles, theories, and practices of strategic marketing, emphasizing its critical role in achieving a competitive advantage in today's dynamic and global business environment. Prerequisites: BUAD2330 and BUAD3240.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students wishing to earn the Data Analytics for Chemistry certificate must complete the following 21 credit hours with Graceland University.
Study of theory and practice of modern separation and analytical techniques. Includes use of electrochemical, spectrometric and chromatographic instruments. Additional fee required. Prerequisite: CHEM1420. Offered odd years Spring.
A study of thermodynamics, thermochemistry, chemical kinetics, equilibrium, atomic and molecular structure, electrochemistry, and quantum chemistry. Additional fee required. Prerequisites: CHEM1420, PHYS1420, and MATH1520. Offered odd years Fall. +This course is only offered every other year.
Additional fee required. Continuation of CHEM3610, which is a prerequisite. Offered even years Spring. +This course is only offered every other year.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Study of theory and practice of modern separation and analytical techniques. Includes use of electrochemical, spectrometric and chromatographic instruments. Additional fee required. Prerequisite: CHEM1420. Offered odd years Spring.
A study of thermodynamics, thermochemistry, chemical kinetics, equilibrium, atomic and molecular structure, electrochemistry, and quantum chemistry. Additional fee required. Prerequisites: CHEM1420, PHYS1420, and MATH1520. Offered odd years Fall. +This course is only offered every other year.
Additional fee required. Continuation of CHEM3610, which is a prerequisite. Offered even years Spring. +This course is only offered every other year.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students wishing to earn the Data Analytics for Computer Science and Information Technology certificate must complete the following 18 credit hours with Graceland University.
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.)
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
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.)
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students wishing to earn the Data Analytics for Economics certificate must complete the following 18 credit hours with Graceland University.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
An application of economic theory to the business of sports. Areas include labor economics, public finance, and the theory of the firm. Prerequisite: ECON1320 and either two MATH courses or MATH1360.
Managerial Economics is a course that explores applying economic theories and methodologies to solve business problems and make informed leadership decisions. It focuses on analyzing economic data, understanding market structures, forecasting demand and supply, and evaluating various business strategies in different economic environments.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
An application of economic theory to the business of sports. Areas include labor economics, public finance, and the theory of the firm. Prerequisite: ECON1320 and either two MATH courses or MATH1360.
Managerial Economics is a course that explores applying economic theories and methodologies to solve business problems and make informed leadership decisions. It focuses on analyzing economic data, understanding market structures, forecasting demand and supply, and evaluating various business strategies in different economic environments.
Students wishing to earn the Data Analytics for Environmental Science certificate must complete the following 20-21 credit hours with Graceland University.
An exploration of the biotic and abiotic components of the environment, including the biological, physical, and chemical processes that shape natural ecosystems (e.g., biogeochemical cycles). The course will also examine the impact of human population growth, resource use, emissions production, and technological innovations on the environment. Current environmental issues, such as loss of biodiversity, ecosystem degradation, air and water pollution, and climate change, will be considered. Additional fee required. ELO6 Science - Innovation, GE3D Liberal Learning-Natural Sciences
A study of how organisms interact with one another and with their physical environments at the physiological, population, community, and ecosystem levels. Case studies will use ecological concepts to develop conservation strategies for species, habitats, and ecosystems. Includes a lab. Additional fee required. EL06 Science - World Citizenship, ELO6 Science - Sustainability +This course is only offered every other year.
An exploration of the biotic and abiotic components of the environment, including the biological, physical, and chemical processes that shape natural ecosystems (e.g., biogeochemical cycles). The course will also examine the impact of human population growth, resource use, emissions production, and technological innovations on the environment. Current environmental issues, such as loss of biodiversity, ecosystem degradation, air and water pollution, and climate change, will be considered. Additional fee required. ELO6 Science - Innovation, GE3D Liberal Learning-Natural Sciences.
Study of theory and practice of modern separation and analytical techniques. Includes use of electrochemical, spectrometric and chromatographic instruments. Additional fee required. Prerequisite: CHEM1420. Offered odd years Spring.
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
Students will learn theoretical and practical foundations related to geographic information systems and spatial analysis. Emphasis on teaching students to integrate and analyze spatial information from various sources. Includes a weekly laboratory section. Prerequisite: MATH1380.
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students will learn theoretical and practical foundations related to geographic information systems and spatial analysis. Emphasis on teaching students to integrate and analyze spatial information from various sources. Includes a weekly laboratory section. Prerequisite: MATH1370.
An exploration of the biotic and abiotic components of the environment, including the biological, physical, and chemical processes that shape natural ecosystems (e.g., biogeochemical cycles). The course will also examine the impact of human population growth, resource use, emissions production, and technological innovations on the environment. Current environmental issues, such as loss of biodiversity, ecosystem degradation, air and water pollution, and climate change, will be considered. Additional fee required. ELO6 Science - Innovation, GE3D Liberal Learning-Natural Sciences
A study of how organisms interact with one another and with their physical environments at the physiological, population, community, and ecosystem levels. Case studies will use ecological concepts to develop conservation strategies for species, habitats, and ecosystems. Includes a lab. Additional fee required. EL06 Science - World Citizenship, ELO6 Science - Sustainability +This course is only offered every other year.
An exploration of the biotic and abiotic components of the environment, including the biological, physical, and chemical processes that shape natural ecosystems (e.g., biogeochemical cycles). The course will also examine the impact of human population growth, resource use, emissions production, and technological innovations on the environment. Current environmental issues, such as loss of biodiversity, ecosystem degradation, air and water pollution, and climate change, will be considered. Additional fee required. ELO6 Science - Innovation, GE3D Liberal Learning-Natural Sciences.
Study of theory and practice of modern separation and analytical techniques. Includes use of electrochemical, spectrometric and chromatographic instruments. Additional fee required. Prerequisite: CHEM1420. Offered odd years Spring.
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
Students will learn theoretical and practical foundations related to geographic information systems and spatial analysis. Emphasis on teaching students to integrate and analyze spatial information from various sources. Includes a weekly laboratory section. Prerequisite: MATH1380.
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
Students will learn theoretical and practical foundations related to geographic information systems and spatial analysis. Emphasis on teaching students to integrate and analyze spatial information from various sources. Includes a weekly laboratory section. Prerequisite: MATH1370.
Students wishing to earn the Data Analytics for Health and Movement Science certificate must complete the following 18 credit hours with Graceland University.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
A foundational course designed for students to become informed about health as well as becoming responsible and active participants in the maintenance of their personal health and affecting the health of their community. The course is intended to provide coverage of health promotion, mental health, stress management, afflictions and diseases, aging, environmental health, consumerism and health care and promotion. A grade of C or higher required to count toward the Allied Health major. ELO4 Global Learning - Sustainability.
Presentation of introductory research and writing methods. Introduction to the application of evidence-based practice using various tools to evaluate the research as evidence. This class will result in a final critically appraised topic paper and poster presentation. A grade of C or higher required to count toward the Allied Health major.
A systematic study of the bones, joints, and muscles of the human body as well as internal external forces initiating and modifying movement. Prerequisite: BIOL2300 or BIOL3420 with a grade of "C" or higher. A grade of C or higher required to count toward the Allied Health major.
The principles and practices of energizing the human body for physical exercise. Prerequisite: BIOL2300 or BIOL3440 with a grade of "C" or better. A grade of C or higher required to count toward the Allied Health major.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
A foundational course designed for students to become informed about health as well as becoming responsible and active participants in the maintenance of their personal health and affecting the health of their community. The course is intended to provide coverage of health promotion, mental health, stress management, afflictions and diseases, aging, environmental health, consumerism and health care and promotion. A grade of C or higher required to count toward the Allied Health major. ELO4 Global Learning - Sustainability.
Presentation of introductory research and writing methods. Introduction to the application of evidence-based practice using various tools to evaluate the research as evidence. This class will result in a final critically appraised topic paper and poster presentation. A grade of C or higher required to count toward the Allied Health major.
A systematic study of the bones, joints, and muscles of the human body as well as internal external forces initiating and modifying movement. Prerequisite: BIOL2300 or BIOL3420 with a grade of "C" or higher. A grade of C or higher required to count toward the Allied Health major.
The principles and practices of energizing the human body for physical exercise. Prerequisite: BIOL2300 or BIOL3440 with a grade of "C" or better. A grade of C or higher required to count toward the Allied Health major.
Students wishing to earn the Data Analytics for Sport Marketing certificate must complete the following 18 credit hours with Graceland University.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
An application of economic theory to the business of sports. Areas include labor economics, public finance, and the theory of the firm. Prerequisite: ECON1320 and either two MATH courses or MATH1360.
An analysis of the field of marketing from a sports perspective with focus on the elements of and development of a marketing plan. Prerequisite: ECON1320.
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
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.
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
An introduction to economic data and statistical techniques commonly applied in business settings. Topics include understanding the basics of data interpretation, manipulation, and visualization. Students will learn how to carry out and interpret basic linear regression and other methods of statistical analysis in Excel.
An application of economic theory to the business of sports. Areas include labor economics, public finance, and the theory of the firm. Prerequisite: ECON1320 and either two MATH courses or MATH1360.
An analysis of the field of marketing from a sports perspective with focus on the elements of and development of a marketing plan. Prerequisite: ECON1320.
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.
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 programming. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5300 Introduction to Data Science.)
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.)
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. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5330 Extracting and Transforming Data.)
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
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.
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 programming. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5300 Introduction to Data Science.)
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.)
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. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5330 Extracting and Transforming Data.)
The course covers the basic aspects of a complete data analytics project. Students will use data sets obtained from community partners. Students will work in teams with each team producing a problem definition in conjunction with the client, conducting the proposed analysis directed at providing insight into the problem, and disseminating the results of the analysis in written and oral form.
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