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This course is designed to provide students with advanced knowledge and practical skills in natural language processing (NLP) research and applications. Students will delve into cutting-edge techniques, methodologies, and tools used in NLP, with a focus on applied research and real-world use cases. Through a combination of lectures, hands-on projects, and literature review assignments, students will explore topics such as text classification, sentiment analysis, named entity recognition, machine translation, question answering, and more. Emphasis will be placed on understanding the underlying algorithms, evaluating model performance, and conducting empirical studies to address real-world NLP challenges. Upon successful completion of this course, students will be able to: 1) Evaluate and apply advanced natural language processing models and architectures to solve domain-specific language tasks. 2) Design, implement, and optimize NLP models using state-of-the-art algorithms, evaluation metrics, and experimental design principles. 3) Critically analyze the ethical, societal, and fairness implications of NLP technologies, and propose responsible solutions. 4) Develop NLP systems that extract, generate, and interpret structured and unstructured data for diverse real-world applications. 5) Conduct applied NLP research by formulating hypotheses, implementing models, and interpreting results to advance the field.

This course explores non-statistical forecasting and other qualitative research methods. Qualitative research methodologies have become more prevalent in research as a viable and valid form of inquiry, especially as they pertain to human behavior in organizations. Qualitative research techniques examined include ethno methodology; grounded theory; and phenomenological research. Nonparametric statistical analysis will also be examined. By the conclusion of this class, you will gain a solid foundation regarding the qualitative research approach and its various traditions along with their theoretical and applied constructs. This will allow you to prepare a qualitative problem for research as well as structure a valid qualitative research design for conducting the actual research itself (i.e., your doctoral dissertation or future research problems in your area of interest or specialization).

RES 713 : QUANTITATIVE RESEARCH METHODS I DATA MANAGEMENT AND NON-EXPERIMENTAL: This course is a combination of quantitative research methods, multivariate statistics, and forecasting. The course assumes the doctoral student has had a graduate level statistics/quantitative methods course covering parametric statistics and hypothesis testing.

This course is the first of three residencies. The residencies occur simultaneously with coursework throughout the student's doctoral journey. The intent of a residency experience is to provide students with a chance to connect directly with faculty/mentor and fellow students within the doctoral program. Students will attend information sessions, meet with faculty/mentors regarding subject matter and research methodology experts, and present their problem/purpose statement to a review board for feedback and direction. Outcome: Finalisation of project problem and purpose statements.

This course is designed to build an advanced body of knowledge (BOK) that will allow students to utilize an extensive array of complex statistical models, tools, and software applications in the analysis of numerical data. Additionally, students will be able to use these advanced techniques to perform predictive analytics. This course is designed to build upon the non-experimental methods and techniques explored in RES 713. The comparative method will be explored along with the issue of ecological inference, aggregate vs. group assessment, and data reduction. Students will then assess the three main traditions associated with the experimental approach-pre-experiment, quasi-experiment, and the "true" experiment. 1) Advanced Non-experimental and Experimental Quantitative Research Methods 2) Quasi-Experimental Design and Analysis 3) True Experimental Design and Analysis 4) Research across time and space 5) Explanation, Prediction and Simulation 6) Big Data, AI, ML and other inductive Methods and Approaches

Learners will master the art of writing. With the courses previously learned, the learners are bound to connect their PPS to management related concepts. This will help them prepare for the comprehensive examination. Comprehensive exams (commonly called “comps”) are an evaluation that measures a doctoral student’s competency and mastery of concepts after completing coursework.Walsh doctoral students are required to take their comprehensive exam upon completion of all their coursework except DIS courses.Passing a comprehensive exam indicates that you are prepared to move into the dissertation phase of the degree.

This dissertation program provides the structure and resources to complete your doctoral research and successfully defend your thesis. *Prerequisite for Dissertation Course: Passing comprehensive exam* 1) Dissertation I - DIS 796 - Chapter 1: Develop a strong foundation for your dissertation. Learn to write a compelling introduction, craft a clear research question, and define your methodology. 2) Dissertation II - DIS 797- Chapter 2: Understand the literature review. Explore relevant research, identify theoretical frameworks, and demonstrate your understanding of the existing scholarship. 3) Dissertation III- DIS 798- Chapter 3: Focus on your research methodology. Refine your data collection plan, discuss data analysis techniques, and ensure ethical research practices. *Prerequisite to move on to Chapter 4 and Chapter 5: Approval from Dissertation Committee, ARB, and IRB* 4) Dissertation IV-DIS 799- Chapter 4: Results: Analyze your research data. Interpret findings, draw conclusions, and identify potential limitations. 5) Dissertation V-DIS 800 - Chapter 5: Write your discussion and conclusion. Integrate your findings with existing literature, discuss the study's significance, and outline future research directions.

Advance your dissertation research and writing. Analyze your data, draft your dissertation chapters, and receive ongoing feedback from your dissertation committee. Residency III will help you prepare for the final oral defence. Dissertation Defense 1) All dissertation committee members must attend 2) Learner will formally present and defend their dissertation findings 3) Dissertation Committee will adjourn to a private session and respond with: a) Pass – No Notations b) Pass – Notations for correction must be corrected before pass status granted c) Hold – Significant notations and issues must be resolved After successful completion of the defense: The Doctoral Program Director and Chief Academic Officer will review all documents and formally sign off on the dissertation.