Faculty Profile

Schenita Floyd

Title
Clinical Assistant Professor
Department
ADA - Advanced Data Analytics
College
University of North Texas

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, University of North Texas, 2021. 
Major: Information Science
Degree Specialization: Data Science
Dissertation Title: Artificial Intelligence in a Collaborative Information Seeking Environment from the Perspective of Women Engineers in the United States
Certificate, George Washington University, 2001. 
Major: Project Management
Degree Specialization: Project Management
Dissertation Title: NA
MBA, Southeastern University, 2000. 
Major: Business
Degree Specialization: Finance
Dissertation Title: N/A
BS, Texas A&M University, 1997. 
Major: Electrical Engineering
Degree Specialization: Control Systems and Telecommunications
Dissertation Title: NA





          

        
                

          
Current Scheduled Teaching*


          
ADTA 5840.001, Agile Frameworks for Analytics, Spring 2024 Syllabus
ADTA 5240.004, Harvesting, Storing and Retrieving Data, Spring 2024 Syllabus
IPAC 4240.700, Principles of Data Structures, Harvesting and Wrangling, Spring 2024 Syllabus


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  


    

    

      


        


          


            
Previous Scheduled Teaching*


            
                    

                    ADTA 5240.001, Harvesting, Storing and Retrieving Data, Fall 2023 Syllabus SPOT
                    

                    ADTA 5240.011, Harvesting, Storing and Retrieving Data, Fall 2023 Syllabus SPOT
                    

                    ADTA 5240.110, Harvesting, Storing and Retrieving Data, Fall 8W2 2023 Syllabus SPOT
                    

                    ADTA 5810.501, Managing Analytics Projects, Fall 2023 Syllabus SPOT
                    

                    ADTA 5240.100, Harvesting, Storing and Retrieving Data, Summer 8W1 2023 Syllabus SPOT
                    

                    IPAC 4240.100, Principles of Data Structures, Harvesting and Wrangling, Summer 8W1 2023 Syllabus SPOT
                    

                    ADTA 5900.110, Special Problems, Summer 8W1 2023 
                    

                    ADTA 5340.001, Discovery and Learning with Big Data, Spring 2023 Syllabus SPOT
                    

                    ADTA 5340.501, Discovery and Learning with Big Data, Spring 2023 Syllabus SPOT
                    

                    ADTA 5340B.001, Exploratory Data Analysis, Visualization, & Supervised Machine Learning, Spring 4W2 2023 Syllabus
                    

                    ADTA 5340A.001, Exploring Machine Learning and AI, Spring 4W4 2023 
                    

                    IPAC 4340.700, Methods for Discovery and Learning from Data, Spring 8W2 2023 Syllabus SPOT
                    

                    ADTA 5340.001, Discovery and Learning with Big Data, Fall 2022 Syllabus SPOT
                    

                    ADTA 5340.501, Discovery and Learning with Big Data, Fall 2022 Syllabus SPOT
                    

                    ADTA 5240A.001, Fundamentals of Data Engineering, Fall 4W2 2022 Syllabus
                    

                    ADTA 5240.001, Harvesting, Storing and Retrieving Data, Fall 2022 Syllabus SPOT
                    

                    IPAC 4340.001, Methods for Discovery and Learning from Data, Fall 2022 Syllabus SPOT
                    

                    ADTA 5340.110, Discovery and Learning with Big Data, Summer 8W2 2022 Syllabus SPOT
                    

                    IPAC 4340.110, Methods for Discovery and Learning from Data, Summer 8W2 2022 Syllabus SPOT
                    

                    ADTA 5340.001, Discovery and Learning with Big Data, Spring 2022 Syllabus SPOT
                    

                    ADTA 5340.501, Discovery and Learning with Big Data, Spring 2022 Syllabus SPOT
                    

                    IPAC 4340.001, Methods for Discovery and Learning from Data, Spring 2022 Syllabus SPOT
                    

                    IPAC 4340.501, Methods for Discovery and Learning from Data, Spring 2022 Syllabus SPOT

          


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  

  
  	

	

	  

		

		   
Published Publications



  

  
Published Intellectual Contributions


  
Journal Article
Saavedra, C., Floyd, S., Mohammad, M., Ivester, R.  (2022). From PMO to APO: The Transformation of a  Project Management Team. PM World Journal. 11(6), 1-17. https://pmworldlibrary.net/wp-content/uploads/2022/05/pmwj118-Jun2022-Saavedra-et-al-from-pmo-to-apo-transformation-of-pm-team.pdf
Floyd, S.  (2021). Assessing African American Women Engineers’ Workplace Sentiment within the AI Field. The International Journal of Information, Diversity, & Inclusion. (5:5), 1-12. https://doi.org/10.33137/ijidi.v5i5.34765
Floyd, S.  (2016). Do machines hold a key to business success. PM World Journal. 5(10), 1-10. https://pmworldlibrary.net/wp-content/uploads/2016/10/pmwj51-Oct2016-Floyd-machines-hold-key-to-business-success-second-edition.pdf



	
		
		
	  

	

	

  
    
	

	

	  

		

		   	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					Overall
Summative Rating		
					Challenge and
Engagement Index		
					Response Rate
					
				

			
			
				

					
					
out of 5		
					
out of 7		
					%
 of 
students responded		
					
					
				

			
		


		
    
			
  • Overall Summative Rating (median):
    
			This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
				
      
					
    • The course as a whole was
      • 
					
    • The course content was
      • 
					
    • The instructor’s contribution to the course was
      • 
					
    • The instructor’s effectiveness in teaching the subject matter was
      • 
				
    
			
    • 
			
  • Challenge and Engagement Index:
    
			This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
				
      
					
    • Do you expect your grade in this course to be
      • 
					
    • The intellectual challenge presented was
      • 
					
    • The amount of effort you put into this course was
      • 
					
    • The amount of effort to succeed in this course was
      • 
					
    • Your involvement in course (doing assignments, attending classes, etc.) was
      • 
				
    
			
    • 
		

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