Master in Data Science and Analytics (MSDSA)

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 improvement.

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.