The Master of Science (MS) in Data Science and Analytics will prepare students to transform data into information so that insight is gained for addressing real world problems. The degree requirements consist of a core set of courses in statistical analysis, basic programming, machine learning, data extraction and transformation, and methods of data science. Students will also choose an Emphasis area in which a certificate will be awarded. Students may complete more than one Emphasis 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, 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.
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 Data Science and Analytics.
Eligibility Requirement: Students matriculating to Graceland will have a high school GPA of 3.0 or higher on a 4.0 scale and have demonstrated an interest in science, mathematics and/or computer science. Students must complete an application for evaluation that addresses these requirements.
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 21 semester hours in the Core Requirements, 9 semester hours in an Emphasis, and 3 semester hours in a Capstone experience. The total hours required to attain the MS degree is 33 semester hours. Each course will be offered in 8 week segments on a schedule to be determined.
Core Requirements (21 semester hours) and Capstone Experience (3 semester hours)
Machine Learning Emphasis (9 semester hours chosen from the following)
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.
An introduction to computer systems, architecture and programming for data science. Coverage includes data structures, algorithms, analysis of algorithms, algorithmic complexity, programming using test-driven design, use of debuggers and profilers, code organization, and version control. Additional topics include data science web applications, SQL, and distributed computing. 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 the machine learning in a practical context. Methods such as ridge and lasso regression, cross-validation, support vector machines, decision trees, and ensemble methods, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Pre- or Corequisite: DSCI5300.
This course will provide students with an understanding of the field of data mining and knowledge discovery in data. Students will become familiar with the foundations of data mining through exploring real-world use cases and cutting edge research in data mining published in academic journals and conferences from various perspectives. Students will also gain hands on experience with data mining tools combined with machine learning and visualization functions. Pre- or Corequisite: DSCI5340.
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.
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.
An introduction to computer systems, architecture and programming for data science. Coverage includes data structures, algorithms, analysis of algorithms, algorithmic complexity, programming using test-driven design, use of debuggers and profilers, code organization, and version control. Additional topics include data science web applications, SQL, and distributed computing. 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 the machine learning in a practical context. Methods such as ridge and lasso regression, cross-validation, support vector machines, decision trees, and ensemble methods, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Pre- or Corequisite: DSCI5300.
This course will provide students with an understanding of the field of data mining and knowledge discovery in data. Students will become familiar with the foundations of data mining through exploring real-world use cases and cutting edge research in data mining published in academic journals and conferences from various perspectives. Students will also gain hands on experience with data mining tools combined with machine learning and visualization functions. Pre- or Corequisite: DSCI5340.
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.
