The Master of Science (MS) in Artificial Intelligence, Data Science, and Data Analytics will prepare students for current industry demands and educational goals. The degree requirements include concentrations that offer targeted courses and industry-relevant certifications, enabling students to pursue specialized expertise while maintaining a robust core foundation of 9 essential courses (27 credits). Students will choose a Concentration area (Cyber and Homeland Security or Nutrition and Health) in which a certificate will be awarded. Students may complete more than one Concentration for certificate recognition.
Applicants must:
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 and GPA requirement.
Students meeting an established set of eligibility requirements upon matriculation to Graceland University will, upon request, gain early acceptance into the 4+1 Masters program in Artificial Intelligence, Data Science, and Data Analytics.
Continuing Requirements: Students in the Early Acceptance program will:
Failure to meet these requirements may impact a student’s status in the Early Acceptance program.
To qualify for graduation, candidates for a graduate degree must:
The MS degree requirements include 27 semester hours in the Core Requirements and 9 semester hours in a declared Concentration (Cyber and Homeland Security or Nutrition and Health) as prescribed below. The total hours required to attain the MS degree is 36 semester hours. Each course will be offered in 8-week segments on a schedule to be determined.
Core Requirements (27 semester hours)
Cyber and Homeland Security Concentration (9 semester hours)
Nutrition and Health Concentration (9 semester hours)
Acquire the skills to defend against cyber threats through advanced network monitoring, malware analysis, and forensic tools. Learn how to investigate cyber incidents, recover digital evidence, and analyze network traffic to detect and respond to attacks Build proficiency in digital forensics and incident response, preparing for a career in cybersecurity and forensic investigation.
Gain proficiency in identifying system and network vulnerabilities using ethical hacking tools such as Metasploit and Burp Suite. Conduct detailed penetration tests to evaluate security measures and provide practical recommendations for improvement. Uphold ethical standards and confidentiality throughout the testing process to ensure responsible and secure practices.
A theoretical and conceptual framework of how domestic and international terrorism arises and functions. Topics discussed will include theories of the world's best terrorist analysts, the historical background on the phenomenon of terrorism, the roots of contemporary conflicts, current conflicts shaping the world stage, emerging groups, and US Homeland Security organizations-including controversies surrounding human rights and protecting civil liberties.
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.
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. Pre- or Corequisite: DSCI5300
Students will learn skills of data acquisition, methods, of data cleaning, imputing data, data storage and other important issues required to producing usable data sets. Code books, data standards, and markdown files will be introduced as well as the concept of the data lake. Pre- or Corequisite: DSCI5300.
This course covers the fundamentals of probability theory and statistical inference used in data science. Students will be introduction to statistical modeling including linear regression models, and generalized linear regression models. Pre- or Corequisite: DSCI5300.
A modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include application of linear regression, general linear models, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced and applied. Pre- or Corequisite: DSCI5340.
The course covers the most often used methods of machine learning and data mining in a practical context. Methods such as ridge and lasso regression, cross-validation, support vector machines, decision trees, clustering, association rules, similarity, dissimilarity, and ensemble methods, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Prerequisite: DSCI5300
This course will cover fundamental concepts of artificial intelligence including algorithms and tools as well their real-world applications. Topics include intelligent agents, knowledge reasoning, learning, and AI problem solving in vision, language, robotics, medicine, etc. Special emphasis is placed on how AI technologies transform businesses and our day-to-day lives by influencing society’s values. Pre- or Corequisite: DSCI5330.
This course provides students with an understanding of the field of large scale data analytics using a high performance, cloud computing data analytics framework. Students will analyze public datasets, network data, and non-structured, steaming dataset. Students will work on real-world cases, learn how to process the data to find valuable insights, and present solutions or suggestions for these cases. Students are encouraged to utilize free and commonly used Open Source Big Data framework and NoSQL database tools. Pre- or Corequisite: DSCI5330.
This introductory course will address the relations between air pollution and human health and the environment in the context of statistical and regression analysis. Specific areas include air toxic monitoring, particulate matter and indoor air pollution. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
This introductory course will address the relationship between water pollution and nutrients in the context of statistical and regression analysis. Specific areas include urban and rural fertilizer application, soil partitioning and moisture, remote sensing and imaging. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
This course will examine the data science behind the chemical emissions models used to predict episodes of photochemical smog, acid deposition, algal blooms, and other environmental events. The goal of the course is to develop augmented modeling methods for application to regional issues. Prerequisite: DSCI5500 and DCSI5520 or permission of instructor.
Students work in conjunction with a supervisor in industry on current problem of importance. The student will gain experience with real – world problems, client presentations, and written data communications. The supervisor, student, and faculty advisor will construct a project plan with expected accomplishments. The supervisor will provide written feedback on the student including an assessment of how the student performed in meeting the expected accomplishments. The faculty member will be responsible for assigning the final grade. To receive credit the project must take 8 weeks to complete. May be repeated once for Emphasis credit with permission. Prerequisite: DCSI5400 or permission of the instructor.
Students work with a practicum supervisor in industry or an academic researcher and address a real-world data problem that exercises the skills developed in the program. Students will submit a proposal, weekly status reports, and a final paper and presentation. To receive credit the project must entail at least 115 hours of work and typically takes 8 weeks to complete. Prerequisites: Completion of 27 semester hours of coursework.
Designed to explore physiological responses and adaptations to training and performance of endurance, strength, and power modes of exercise. Emphasis is on the metabolic, neurological, endocrine, cardiovascular, and respiratory systems. This course is designed to apply physical activity and exercise training principles to sedentary, active, and athletic populations. Offered Fall semester.
Examining a detailed study of human physiology and biochemistry of vitamins and minerals, their relationship with proteins, carbohydrates, and lipids, and how nutrition influences metabolism, and cellular function through the lifecycle. Micronutrients and macronutrients will be discussed in relation to ingestion, digestion, absorption, transportation, and metabolism. The clinical signs and symptoms of nutrition-related disorders and treatments such as adequate diets and Recommended Daily Allowances will be discussed. Offered Spring semester.
This course is designed to introduce students to the background, basic principles, and methods of health epidemiology, with an emphasis on critical thinking, analytic skills, and application to clinical practice. Topics covered in this course include basic principles of epidemiology; measures of disease frequency; epidemiologic study designs: experimental and observational; bias; confounding; outbreak investigations; screening; causality; and ethical issues in epidemiologic research. In addition, students will develop skills to read, interpret and evaluate health information from published epidemiologic studies. Offered Summer semester.
Acquire the skills to defend against cyber threats through advanced network monitoring, malware analysis, and forensic tools. Learn how to investigate cyber incidents, recover digital evidence, and analyze network traffic to detect and respond to attacks Build proficiency in digital forensics and incident response, preparing for a career in cybersecurity and forensic investigation.
Gain proficiency in identifying system and network vulnerabilities using ethical hacking tools such as Metasploit and Burp Suite. Conduct detailed penetration tests to evaluate security measures and provide practical recommendations for improvement. Uphold ethical standards and confidentiality throughout the testing process to ensure responsible and secure practices.
A theoretical and conceptual framework of how domestic and international terrorism arises and functions. Topics discussed will include theories of the world's best terrorist analysts, the historical background on the phenomenon of terrorism, the roots of contemporary conflicts, current conflicts shaping the world stage, emerging groups, and US Homeland Security organizations-including controversies surrounding human rights and protecting civil liberties.
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.
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. Pre- or Corequisite: DSCI5300
Students will learn skills of data acquisition, methods, of data cleaning, imputing data, data storage and other important issues required to producing usable data sets. Code books, data standards, and markdown files will be introduced as well as the concept of the data lake. Pre- or Corequisite: DSCI5300.
This course covers the fundamentals of probability theory and statistical inference used in data science. Students will be introduction to statistical modeling including linear regression models, and generalized linear regression models. Pre- or Corequisite: DSCI5300.
A modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include application of linear regression, general linear models, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced and applied. Pre- or Corequisite: DSCI5340.
The course covers the most often used methods of machine learning and data mining in a practical context. Methods such as ridge and lasso regression, cross-validation, support vector machines, decision trees, clustering, association rules, similarity, dissimilarity, and ensemble methods, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Prerequisite: DSCI5300
This course will cover fundamental concepts of artificial intelligence including algorithms and tools as well their real-world applications. Topics include intelligent agents, knowledge reasoning, learning, and AI problem solving in vision, language, robotics, medicine, etc. Special emphasis is placed on how AI technologies transform businesses and our day-to-day lives by influencing society’s values. Pre- or Corequisite: DSCI5330.
This course provides students with an understanding of the field of large scale data analytics using a high performance, cloud computing data analytics framework. Students will analyze public datasets, network data, and non-structured, steaming dataset. Students will work on real-world cases, learn how to process the data to find valuable insights, and present solutions or suggestions for these cases. Students are encouraged to utilize free and commonly used Open Source Big Data framework and NoSQL database tools. Pre- or Corequisite: DSCI5330.
This introductory course will address the relations between air pollution and human health and the environment in the context of statistical and regression analysis. Specific areas include air toxic monitoring, particulate matter and indoor air pollution. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
This introductory course will address the relationship between water pollution and nutrients in the context of statistical and regression analysis. Specific areas include urban and rural fertilizer application, soil partitioning and moisture, remote sensing and imaging. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.
This course will examine the data science behind the chemical emissions models used to predict episodes of photochemical smog, acid deposition, algal blooms, and other environmental events. The goal of the course is to develop augmented modeling methods for application to regional issues. Prerequisite: DSCI5500 and DCSI5520 or permission of instructor.
Students work in conjunction with a supervisor in industry on current problem of importance. The student will gain experience with real – world problems, client presentations, and written data communications. The supervisor, student, and faculty advisor will construct a project plan with expected accomplishments. The supervisor will provide written feedback on the student including an assessment of how the student performed in meeting the expected accomplishments. The faculty member will be responsible for assigning the final grade. To receive credit the project must take 8 weeks to complete. May be repeated once for Emphasis credit with permission. Prerequisite: DCSI5400 or permission of the instructor.
Students work with a practicum supervisor in industry or an academic researcher and address a real-world data problem that exercises the skills developed in the program. Students will submit a proposal, weekly status reports, and a final paper and presentation. To receive credit the project must entail at least 115 hours of work and typically takes 8 weeks to complete. Prerequisites: Completion of 27 semester hours of coursework.
Designed to explore physiological responses and adaptations to training and performance of endurance, strength, and power modes of exercise. Emphasis is on the metabolic, neurological, endocrine, cardiovascular, and respiratory systems. This course is designed to apply physical activity and exercise training principles to sedentary, active, and athletic populations. Offered Fall semester.
Examining a detailed study of human physiology and biochemistry of vitamins and minerals, their relationship with proteins, carbohydrates, and lipids, and how nutrition influences metabolism, and cellular function through the lifecycle. Micronutrients and macronutrients will be discussed in relation to ingestion, digestion, absorption, transportation, and metabolism. The clinical signs and symptoms of nutrition-related disorders and treatments such as adequate diets and Recommended Daily Allowances will be discussed. Offered Spring semester.
This course is designed to introduce students to the background, basic principles, and methods of health epidemiology, with an emphasis on critical thinking, analytic skills, and application to clinical practice. Topics covered in this course include basic principles of epidemiology; measures of disease frequency; epidemiologic study designs: experimental and observational; bias; confounding; outbreak investigations; screening; causality; and ethical issues in epidemiologic research. In addition, students will develop skills to read, interpret and evaluate health information from published epidemiologic studies. Offered Summer semester.
