Master in Data Science and Analytics (MSDSA) (formerly Master of Science in Applied Statistics and Analytics)
The Master of Science in Data Science and Analytics (MSDSA) program at Kennesaw State University (KSU) is a professional degree program which prepares a diverse student body to utilize cutting edge data science and analytics techniques for careers in business, industry, government, and health care. Students graduate with the essential knowledge, techniques, and tools gained from working with faculty who integrate real-world data into their courses and multiple opportunities to work directly with corporate partners.Apply Now »
About MSDSA Program
The MSDSA program is a 36 semester-hour graduate program designed to help students gain the skills, knowledge, and experiences they need to be practicing data scientists and analytics professionals. Our students come from wide variety of academic and professional backgrounds, but by the time they graduate all are prepared for a career in data science and analytics. Highlights of the program include:
- Fluency in analytical programming languages such as R, Python, and SAS, all necessary for the workforce.
- Statistical and mathematical foundations emphasized throughout the program as essential to understanding model building.
- Working directly with companies every semester in sponsored project courses is possible. Previous sponsors include Equifax, Travelers, Southern Company, IHG, Shaw Flooring, Trusit Bank, Spanx, Atlanticus, and Coke One North America.
- Capstone options include internships, applied research projects, research with faculty, working in a Center for Data Science and Analytics sponsored lab, and others.
- Emphasis on communication of results and professional presentation skills, which is vital and helps our graduates stand out in job searches.
- Analytics Day each fall and spring brings actively recruiting employers and industry professionals directly to the students in our programs who get to showcase their skills through poster presentations.
- Students can take up to 9 hours of related courses in other programs at KSU to help expand their knowledge and skills into related areas like Information Technology, Computer Science, Software Engineering, Business, Health, Social Sciences, and Education.
Curriculum - Program Requirements
Required Courses (12 Credit Hours)
- STAT 7010 - Mathematical Statistics I
- STAT 7020 - Statistical Computing and Simulation
- STAT 7100 - Statistical Methods
- STAT 7210 - Applied Regression Analysis
Select one from the following (3 Credit Hours)
- STAT 7220 - Applied Experimental Design
- STAT 7125 - Design and Analysis of Human Studies
Select at least two from the following (6 Credit Hours)
- STAT 7220 - Applied Experimental Design (if not selected above)
- STAT 7125 - Design and Analysis of Human Studies (if not selected above)
- STAT 8220 - Time Series Forecasting
- STAT 7225 - Applied Longitudinal Data Analysis
- STAT 8240 - Data Mining
- STAT 7310 - Applied Categorical Data Analysis
- STAT 8320 - Applied Multivariate Data Analysis
- STAT 8330 - Applied Binary Classification
Required Project (6 to 9 Credit Hours)
Minimum of 6 credit hours are required. Students can take any of the courses here multiple times for credits. But maximally 9 credit hours can be applied for the degree. A written report (a project proposal, a project status update, or a final project report) is required by the end of each semester when any amount of the credits are taken.
- STAT 7916: Cooperative Education
- STAT 7918: Internship
- STAT 7940: Applied Analysis Project
Any other course with a STAT prefix (with the exception of STAT 9100 and STAT 9200) may be used to complete the degree requirements.
- STAT 7900 - Special Topics
- STAT 7120 - Advanced Programming in SAS
- STAT 7130 - Programming in R
- STAT 7110 - Quality Control and Process Improvement
- STAT 7140 - Six Sigma Problem Solving
- STAT 8250 - Data Mining II
Note: Up to nine hours may be substituted with the permission of the Program Coordinator.
Program Total (36 Credit Hours)KSU CATALOG »
MSDSA - Course Forecast
- 7020 (Base SAS) — Required MSAS
- 7100 (Methods) — Required MSAS
- 7130 (R)
- 7240/8240 (Data Mining)
- 7010 (Math Stat) — Required MSAS
- 7110 (Quality Control) — even years
- 7225 (Longitudinal) — even years
- 7310 (Categorical) — odd years
- 7120 (Advanced SAS)
- 7220 (Design) — Required MSAS
- 7210 (Regression) — Required MSAS
- 8250 (Data Mining II)
- 8330 (Binary Classification)
- 7140 (Six Sigma) — odd years
- 8320 (Multivariate) — odd years
- 8220 (Time Series) — even years
- STAT 7010
- STAT 7020
- STAT 7100
- STAT 7220
- STAT 7210
- STAT 7130
- STAT 7240/8240
- STAT 7120
- STAT 8250
- STAT 8330
STAT 7940 (Applied Analysis Project), STAT 7916 (Co-op) and STAT 7918 (Internship) are offered every term.
STAT 7125 (Human Studies) will be offered according to the lab request.
In general, the following electives will be offered at least as often as indicated below:
- STAT 7110 (Quality Control) – Fall of even numbered years
- STAT 7140 (Six Sigma) – Spring of odd numbered years
- STAT 8220 (Time Series) – Spring of even numbered years
- STAT 7225 (Longitudinal) – Fall of even numbered years
- STAT 7310 (Categorical) – Fall of odd numbered years
- STAT 8320 (Multivariate) – Spring of odd numbered years
- With agreement of the Program Director, graduate courses in other KSU departments can be substituted for up to 9 hours in the above program to complete the required 36 credit hours.
- In addition, some of the courses intended for the Ph.D. in Data Science and Analytics program (which are not shown here) can be taken as electives by students in the MSDSA program. These courses will typically be taught during the day and not at the traditional MSDSA evening time.
- Introduction to Mathematics for Statistics
- Mathematical Statistics
- Statistical Computing and Simulation
- Statistical Methods
- Advanced Programming in SAS
- Quality Control and Process Improvement
- Applied Experimental Design
- Design and Analysis of Human Studies
- Six Sigma Problem Solving
- Applied Regression Analysis
- Time Series Analysis
- Applied Longitudinal Data Analysis
- Data Mining
- Applied Categorical Data Analysis
- Applied Multivariate Methods
- Applied Topics in Binary Classification
- Applied Analysis Project
Find out more information about the admission requirements on the Graduate College's website.
Area of Interest
Categorical Data Analysis | Data Mining | Measurement System Analysis
Modeling Data | Performance Improvement Measures | Quality Control
Six Sigma | Statistical Computing | Statistical Methods
Categorical Data Analysis - Categorical data analysis is an important tool in many areas, particularly biological
and health sciences. This type of analysis is focused on outcomes that either cannot
or should not be studied using a continuous model. The most common type of categorical
analysis is with a binary yes/no outcome such as presence or absence of disease or
success or failure of a process. Since this type of outcome is so common, we will
spend a large proportion of the course working with this sort of data. We will learn
to analyze binary outcomes like this in detail using univariate techniques and logistic
regression. In particular, we will focus on interpreting and reporting binary outcomes
and their predictors in a fashion that makes our results understandable to the end
user. In addition, we will work with techniques for modeling multi-level outcomes
and survival data, which are also common in today's world. We will discuss how to
make decisions about using various categorical models for both predictors and outcomes.
At the end of the course, the students should be able to conduct and report a complete
analysis of several types of categorical outcomes.
Data Mining - The almost ubiquitous presence of electronic data capture through the internet,
e-commerce, electronic banking, point-of-sale devices, bar-code readers, and the like
has created a very data-rich, but information poor decision making environment. Data
mining is a rapidly growing field where the application of statistical tools and artificial
intelligence enables the conversion of data into information to dramatically improve
decision making. Successful applications of data mining include areas such as credit
rating, fraud detection, database marketing, customer relationship management, and
stock market investments.
Measurement System Analysis - It is well-known that many problems in business, industry and government arise from
using data that has unknown and variable precision or accuracy. It is not unusual
to have the measurement system the focus of a process improvement project. In Six
Sigma process improvement methodology, DMAIC, the "M" stands for "Measure". Every
improvement activity must do a thorough analysis of measurement variation (MSA). The
MSDSA program is unique in that an entire course (STAT 7140) is devoted to assessing
and improving the measurement system. Students will learn how to perform measurement
system studies which can lead to process improvements. Data is the focus of most decision
making, collecting data requires measurement. How accurate/variable is your data?
Modeling Data - Statistical models are used in business and economics and in the social, health,
behavioral, biological, physical and engineering sciences. The basic goal of statistical
modeling is to use the information contained in data to develop a mathematical model
describing relationships among the variables being measured. Statistical modeling
extends the concept of mathematical modeling by taking into account the stochastic
(random) nature of the relationships among the variables. The models that are developed
are often used to predict the future behavior of a system, to screen out variables
that are relatively unimportant to the system, to understand better the behavior of
the system or to support or refute a theory about the behavior of the system.
Performance Improvement Measures - How does an organization improve performance? There is no prescription, but there
are accepted practices to achieve Performance Excellence. These practices include
DMAIC from Six Sigma that integrates the use of statistical tools into the performance
improvement process. Criteria for attaining excellence are given by the Malcolm Baldrige
National Quality Award or the Georgia Oglethorpe Award. These criteria will be discussed
in light of their relevance to the use of statistical methods. For all methods, data
play a central role. Decisions must be based on data. Use of statistical methods to
collect, analyze and communicate information on opportunities for improvement will
be discussed as part of MSDSA. Project managers, management and support personnel
will benefit by understanding how to use statistical methods in the context of organizational
improvement. Students will learn that every organizational activity can be considered
a process. A process flow diagram with inputs, value added activity and outputs is
the start for improving a process. Students will have the opportunity to work on a
project that could address improvement of a process within their organization.
Quality Control - Statistical quality control is an indispensable tool in monitoring and improving
the manufacturing process in facilities worldwide. Step by step, from identifying
and constructing all the pieces of a control flow-chart, through creating and interpreting
an appropriate control chart for a process, to designing experiments for process characterization
and optimization, and finally, implementing quality management techniques, such as
the six-sigma approach, this course will prepare students for professional practice
with comprehensive coverage of current statistical methods for quality control and
Six Sigma - Every organization seeks to reduce their costs by improving operating processes.
This is the focus of all Six Sigma projects. Performing a search on Monster.com shows
the wide variety of organizations that utilize Six Sigma methodology. These methods
are organized in a process, DMAIC, which has become a universal improvement methodology.
Learning this methodology enhances a career and can open new career opportunities.
The MSDSA program trains students in Six Sigma methods in all courses since the basis
of Six Sigma is the use of statistical methods. A complete course (STAT 7140) is required
in the program to ensure students can put all the tools together to perform effective
problem solving. Certification as a Six Sigma Black Belt is not required, but optional
in the second year. Students interested in certification will need to have one project
certified as effective cost savings project (with 3 years qualifying experience) and
two projects otherwise. At the end of the first year, students will be prepared to
take the Six Sigma Green Belt certification exam. This option can enhance career opportunities.
Statistical Computing - At KSU, we have incorporated the four most widely used statistical computing packages
into our Statistics courses – EXCEL, SPSS, Minitab and SAS. While each of these packages
can be used for basic data analysis, they each have specializations. Any individual
who can represent themselves as knowledgeable and proficient in any or all of these
packages will possess a marketable and differentiating skillset. EXCEL is used anywhere
that data is available – which is everywhere. EXCEL is found in offices, libraries,
schools, universities, home offices and everywhere in between. In addition to its
role as a data analysis package, EXCEL is often used as a starting point to capture
and organize data and then import it into more sophisticated analysis packages such
as SPSS, Minitab or SAS. And, after analysis is complete, datasets can be exported
back to EXCEL and shared with others who may not have access to (or have the ability
to use) other analysis packages (we gently refer to this group as the "great statistical
unwashed").Microsoft's EXCEL spreadsheet software package is almost ubiquitous. It
represents a very basic and efficient way to organize, analyze and present data. Employers
today expect that at a minimum new hires with college degrees will have a working
knowledge of EXCEL.
Statistical Methods - The MSDSA Program at KSU will equip its graduates with foundational and readily applicable knowledge on all statistical methods most commonly used in business, industry and research. The Mathematical Statistics course (STAT 7010) will introduce the underlying theory (coupled with real-world applications) for the discipline of statistical inference. In these courses, students will learn how to make sound inferences about populations from sample data and why these methods work.
Throughout the program, the statistical software packages introduced in the first semester Statistical Computing course (STAT 7020) will be utilized by students to perform the methods they are learning and to help them analyze the results. In later semesters, students engage in courses specifically geared to provide skills and understanding of statistical methods for multivariate data (STAT 8320) and categorical data (STAT 7310). Similarly, there is a course in Applied Regression Analysis (STAT 7210), the most important methodology in statistical modeling.
Additionally, the student's ongoing work each semester on applied projects in the project course (STAT 7940) will result in further experience in real-world applications of methods mastered in the courses.
Frequently Asked Questions (FAQ)
Here are the most commonly asked questions regarding the Master of Science in Data Science and Analytics (MSDSA) program at KSU.
- Why does MSDSA Admission require Calculus? The depth of understanding of statistics depends on a basic knowledge of calculus.
The focus of the this program is to develop graduates that have in-depth knowledge
of the techniques they will be using. "Plugging into" formulas or computer a routine
is not the objective. This approach will enable students to develop meaningful careers
and be in demand in the marketplace.
- Can I start in the spring, rather than the fall? Yes, this program accepts new students in the spring and fall.
- Can I substitute any graduate course from other departments? Yes, you can substitute up to 9 credit hours from other graduate programs with permission
from the program coordinator.
- Does Kennesaw offer any courses to help me prepare prior to applying to MSDSA? Yes. MATH 1190 (Calculus I), MATH 2202 (Calculus II) and MATH 3260 (Linear Algebra)
are offered every semester. These would prepare students for the calculus part of
the program. Courses in the Minor in Data Science and Analytics (STAT 3010, STAT 3120,
STAT 3130, STAT 4120 and STAT 4210) would help students prepare for the statistics
part of the program. These are not requirements for admission to the program.
- What can I do with my degree? The entry degree for most positions requiring statistical training is the Master of
Science degree. A recent Bureau of Labor Statistics report indicated that 18% of the
country's statisticians work for the federal government, 16% for state and local governments
and the remainder for private industry. University based statisticians are a relatively
small percentage. Thus, a large percentage of Data Science and Analytics graduate
students will likely be placed in the private sector.
- My undergraduate degree is not in math or statistics; can I still enroll in MSDSA? Yes, as long as you have the prerequisite mathematics knowledge (Calculus I and Calcuus
- Is there any financial support for students? Yes. There are a limited number of graduate research assistantships to be awarded.
In addition, financial aid and loans are available.
- How much does it cost to enroll in this program? For current tuition costs, visit KSU's Graduate College - Admissions Financial Information webpage »
- I'm not interested in Six Sigma certification. Do I need to take the tests? No. There is no requirement to take any American Society for Quality certification
test. At the end of the first year, students will be ready to take the Green Belt
exam and can do so if they choose. At the end of the program, students will have covered
the Body of Knowledge for the Black Belt exam. MSDSA is unique in that the Body of
Knowledge is addressed for both exams within the MSDSA courses.
- Will Kennesaw State University (KSU) accept my degree from a university outside of
the United States? Graduates of universities outside the United States must be able to document that
their degree is the equivalent of a four-year bachelor's degree awarded by an accredited
United States college or university. For more information, visit KSU's Graduate College - Admissions for International Students webpage »
- What makes this program different from other programs? You will leave this program with skills that you can immediately use in the workplace or to go out and get a job. Our eleven Ph.D. statisticians have knowledge and experience in a wide range of applied settings.