Online MSDSA Program
Master of Science in Data Science and Analytics Degree

Data to Information to Insights

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.

Data science and analytics is an area in which many disciplines and backgrounds can and do participate. Graceland's program takes a very practical and applied learning approach to skill development. The program relies on the use of real data sets to address challenging and current problems and issues.

Master of Science Degree in Data Science and Analytics - The "4+1" Program

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

Questions? Please contact us:

Admissions Team
Graceland University
1401 W. Truman Rd.
Independence, MO. 64050

800-833-0524, ext. 4717
distancelearning@graceland.edu

A Career with Growth Potential

The Bureau of Labor Statistics states that jobs for data scientists will experience a 14% growth through 2028 and report that the average salary is approximately $118,000 annually. Careers in data science and analytics span the spectrum of both public and private companies and organizations.

Master of Science Degree in Data Science and Analytics

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)
DSCI5300 Introduction to Data Science 3 s.h.
DSCI5320 Practical Applications of Data Science 3 s.h.
DSCI5330 Extracting and Transforming Data 3 s.h.
DSCI5340 Probability and Statistical Inference 3 s.h.
DSCI5350 Basics of Computer Algorithms and Databases 3 s.h.
DSCI5360 Regression and Time Series Modeling 3 s.h.
DSCI5370 Machine Learning 3 s.h.
DSCI6000 Data Science Capstone 3 s.h.
Machine Learning Emphasis (9 semester hours chosen from the following)
DSCI5400 Data Mining 3 s.h.
DSCI5420 Artificial Intelligence in Practice 3 s.h.
DSCI5440 Big Data Analytics 3 s.h.
DSCI5700 Internship 3 s.h.
Environmental Analytics Emphasis (9 semester hours chosen from the following)
DSCI5500 Air Pollution and Health Analytics 3 s.h.
DSCI5520 Water Quality and Nutrients 3 s.h.
DSCI5540 Chemical Emissions Modeling 3 s.h.
DSCI5700 Internship

Courses in Data Science (Graduate)

DSCI5300 Introduction to Data Science 3 s.h.
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.

DSCI5320 Practical Applications of Data Science 3 s.h.
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: DSCI5300

DSCI5330 Extracting and Transforming Data 3 s.h.
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. Prerequisites: DSCI5300.

DSCI5340 Probability and Statistical Inference 3 s.h.
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. Prerequisites DSCI5320.

DSCI5350 Basics of Computer Algorithms and Databases 3 s.h.
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. Prerequisites DSCI5320.

DSCI5360 Regression and Time Series Modeling 3 s.h.
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. Prerequisites: DSCI5340.

DSCI5370 Machine Learning 3 s.h.
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. Prerequisites: DSCI5350.

DSCI5400 Data Mining 3 s.h.
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. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.

DSCI5420 Artificial Intelligence in Practice 3 s.h.
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. Prerequisite: DSCI5340 and DCSI5350 or permission of instructor.

DSCI5440 Big Data Analytics 3 s.h.
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. Prerequisite: DSCI5320 or DSCI5330 or permission of instructor.

DSCI5500 Air Pollution and Health Analytics 3 s.h.
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.

DSCI5520 Water Quality and Nutrients 3 s.h.
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.

DSCI5540 Chemical Emissions Modeling 3 s.h.
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.

DSCI5700 Internship 3 s.h.
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.

DSCI6000 Data Science Capstone 3 s.h.
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: DCSI5370 and completion of 9 semester hours of Emphasis Course work, or permission of the instructor.

Distance Learning MSDSA Program

Tuition and Fees

Fee Cost
Tuition $550.00 per semester hour
Program Support Fee (online courses) $18.00 per course
University Technology Fee $4 per credit hour (online)

For more information regarding additional fees, please see General Fees.

Questions? Please contact us:

Admissions Team
Graceland University
1401 W. Truman Rd.
Independence, MO. 64050

800-833-0524, ext. 4717
distancelearning@graceland.edu

Admission Requirements for the Data Science and Analytics Program

  • Have a minimum GPA of a 3.0 on a 4.0 scale.
  • Hold a Bachelor’s degree or equivalent from an accredited college or university. The Bachelor’s degree can be in any field but it is strongly encouraged that those degrees align with the Emphasis area.
  • Have successfully completion (a grade of “C” or better) in the following courses:
    •     Calculus (minimum of 8 credit hours)
    •     Statistics (minimum of 3 or 4 credit hours)
    •     Programming courses ( minimum of 6 credit hours)
    •     A science course with laboratory (minimum of 4 credit hours)
  • Complete the Graduate Application form (not required for Graceland students)
  • Provide 3 letters of recommendation

Transfer Credits

A maximum of 9 semester hours may be transferred into the program

To qualify for graduation, candidates for a graduate degree must:

  1. Complete sufficient credits to meet the total required graduate semester hours within six years of enrollment. Students may petition for a single one-year extension.
  2. Complete at least 33 total graduate semester hours at Graceland University within one of the degree emphases:
    • Students seeking more than one emphasis must complete all required core curriculum courses totaling 24 semester hours and the additional semester hours for each emphasis desired. A certificate will be awarded for each completed Emphasis.
  3. Earn a final GPA of 3.0 on a 4.0 scale. Students may earn a grade of “C ” or below in one course but a second grade of “C” or below will require a review of the student's performance. Grades of “D” will automatically be reviewed.
  4. Fulfill all financial obligations to Graceland University.
  5. File an application for graduation with the Office of the Registrar at least six months prior to the anticipated graduation date.

Questions? Please contact us:

Admissions Team
Graceland University
1401 W. Truman Rd.
Independence, MO. 64050

800-833-0524, ext. 4717
distancelearning@graceland.edu