This is a North Central University paper about analyzing qualitative software. It is written in APA format, includes references, and is graded an instructor.
Using NVivo QSR Theory and Practice for Qualitative Data Analysis in a PhDKEDGE Business School
Session from Salford Business School http://www.salford.ac.uk/business-school doctoral school at the Digital Business Centre. This explain the rationale and some of the basic concepts when it comes to using NVivo QSR for data analysis.
NVivo is a tool for helping to you analyse qualitative data but it does not replace the thinking process - there is a need for you to consider the bigger picture of how NVivo will fit into your research project and this presentation offers some themes you should explore before you commit to the use of NVivo.
Computer Software in Qualitative Research: An Introduction to NVivoAdam Perzynski, PhD
This document introduces the qualitative data analysis software NVivo. It discusses NVivo's vocabulary, capabilities for managing and analyzing textual data, and advanced tasks. The document also covers example data used in NVivo, demonstrations of its functions, frequently asked questions, debates around computer software for qualitative research, and conclusions about using NVivo and remaining reflexive in the research process.
This document provides an introduction to NVivo, a qualitative data analysis software. It describes how NVivo can be used to organize, analyze, and find insights in unstructured qualitative data like documents, interviews, and social media posts. The document outlines the basic NVivo workspace and functions for importing data sources, coding data, running queries, and visualizing results. It also provides guidance on setting up an NVivo project and includes some example tasks for getting started with the software.
This document provides information about Aleksandra Pawlik's PhD research project which aims to explore how best to support scientific end-user software development. The research will focus on identifying problematic and successful tools/techniques used by scientific developers through case studies of projects that transition from limited to extended contexts or involve software professionals. Qualitative methods like interviews and observation will be used to understand the challenges and how support can be improved.
CIS 532 STUDY Education Planning--cis532study.comShivendrasing2
This document outlines assignments and case studies for a CIS 532 networking class, including papers on network topology design, security planning, and technical term papers. It provides details on 2 papers for each assignment, which involve designing network solutions, addressing security concerns, integrating routing protocols, and developing a network migration plan within budget for a large retail company. Students are to complete networking design documents, presentations, and case study analyses on topics like wireless solutions, business goals, and quality of service requirements.
CIS 532 STUDY Inspiring Innovation--cis532study.comKeatonJennings93
This document outlines the requirements for a technical term paper assignment for a CIS 532 class. It provides background information on a fictional company, Fiction Corporation, which is migrating its primary data center to a new headquarters building. The student is tasked with developing a network design document and plan to upgrade Fiction Corporation's network and address security issues as part of the data center migration, within a budget of $500,000 and without disrupting business operations. The paper should include details on the current network configuration, a logical and physical design for the new network, an implementation plan, and a projected budget and return on investment.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
NVivo is qualitative data analysis software produced by QSR International. It allows users to organize and classify various forms of unstructured data like documents, audio, video and images. NVivo provides tools to help users capture online data, visualize connections in their data, link ideas and themes, and perform mixed methods research. The software aims to replicate the paper-based qualitative analysis process digitally for improved organization and faster work with large datasets.
Using NVivo QSR Theory and Practice for Qualitative Data Analysis in a PhDKEDGE Business School
Session from Salford Business School http://www.salford.ac.uk/business-school doctoral school at the Digital Business Centre. This explain the rationale and some of the basic concepts when it comes to using NVivo QSR for data analysis.
NVivo is a tool for helping to you analyse qualitative data but it does not replace the thinking process - there is a need for you to consider the bigger picture of how NVivo will fit into your research project and this presentation offers some themes you should explore before you commit to the use of NVivo.
Computer Software in Qualitative Research: An Introduction to NVivoAdam Perzynski, PhD
This document introduces the qualitative data analysis software NVivo. It discusses NVivo's vocabulary, capabilities for managing and analyzing textual data, and advanced tasks. The document also covers example data used in NVivo, demonstrations of its functions, frequently asked questions, debates around computer software for qualitative research, and conclusions about using NVivo and remaining reflexive in the research process.
This document provides an introduction to NVivo, a qualitative data analysis software. It describes how NVivo can be used to organize, analyze, and find insights in unstructured qualitative data like documents, interviews, and social media posts. The document outlines the basic NVivo workspace and functions for importing data sources, coding data, running queries, and visualizing results. It also provides guidance on setting up an NVivo project and includes some example tasks for getting started with the software.
This document provides information about Aleksandra Pawlik's PhD research project which aims to explore how best to support scientific end-user software development. The research will focus on identifying problematic and successful tools/techniques used by scientific developers through case studies of projects that transition from limited to extended contexts or involve software professionals. Qualitative methods like interviews and observation will be used to understand the challenges and how support can be improved.
CIS 532 STUDY Education Planning--cis532study.comShivendrasing2
This document outlines assignments and case studies for a CIS 532 networking class, including papers on network topology design, security planning, and technical term papers. It provides details on 2 papers for each assignment, which involve designing network solutions, addressing security concerns, integrating routing protocols, and developing a network migration plan within budget for a large retail company. Students are to complete networking design documents, presentations, and case study analyses on topics like wireless solutions, business goals, and quality of service requirements.
CIS 532 STUDY Inspiring Innovation--cis532study.comKeatonJennings93
This document outlines the requirements for a technical term paper assignment for a CIS 532 class. It provides background information on a fictional company, Fiction Corporation, which is migrating its primary data center to a new headquarters building. The student is tasked with developing a network design document and plan to upgrade Fiction Corporation's network and address security issues as part of the data center migration, within a budget of $500,000 and without disrupting business operations. The paper should include details on the current network configuration, a logical and physical design for the new network, an implementation plan, and a projected budget and return on investment.
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
NVivo is qualitative data analysis software produced by QSR International. It allows users to organize and classify various forms of unstructured data like documents, audio, video and images. NVivo provides tools to help users capture online data, visualize connections in their data, link ideas and themes, and perform mixed methods research. The software aims to replicate the paper-based qualitative analysis process digitally for improved organization and faster work with large datasets.
The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation
in analytical dashboards is a ‘drill-down’, which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.
Directed versus undirected network analysis of student essaysRoy Clariana
IWALS 2018
6th International Workshop on Advanced Learning Sciences
Perspectives on the Learner: Cognition, Brain, and Education
University of Pittsburgh, USA JUNE 6-8, 2018
Using a keyword extraction pipeline to understand concepts in future work sec...Kai Li
This document describes a study that uses natural language processing and text mining techniques to identify future work statements in scientific papers and extract keywords from those statements. The researchers developed a multi-step pipeline to first identify the future work section, then select future work sentences within that section. They used rules and algorithms to identify sentences discussing future work. Keywords were then extracted from the selected sentences using the RAKE algorithm. An analysis found that 31.4% of papers contained future work statements, with medical science papers having the highest overlap between future work and title-abstract keywords. The researchers hope this work is a first step toward predicting future research topics.
This Tutorial contains 2 Set of Papers for each Assignment
CIS 532 Week 2 Assignment 1 Request for Proposal Response (2 Papers)
CIS 532 Week 3 Case Study 1 Harriet’s Fruit and Chocolate Company (2 Papers)
Workshop 2 using nvivo 12 for qualitative data analysisDr. Yaar Muhammad
This document provides an overview of using NVivo 12 for qualitative data analysis. It discusses the seven key stages of qualitative analysis: 1) importing data, 2) coding data, 3) creating framework matrices, 4) reporting findings. It describes how to import various file types into NVivo and code data using both first and second cycle coding methods. Framework matrices allow for analyzing patterns across cases. Well supported assertions should be used to report the findings of the qualitative analysis.
For more course tutorials visit
www.tutorialrank.com
This Tutorial contains 2 Set of Papers for each Assignment
CIS 532 Week 2 Assignment 1 Request for Proposal Response (2 Papers)
CIS 532 Week 3 Case Study 1 Harriet’s Fruit and Chocolate Company (2 Papers)
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
Group X analyzed data using computer software. They discussed several types of software for analyzing qualitative data, including those for coding text, developing theories, and building conceptual networks. The functions to look for include coding, memoing, searching, and displaying data. There is no single best software; the researcher must consider their data, approach, and needs. The document provided examples of research articles that used different software like MS Word, NVivo, and Qualrus to analyze qualitative data.
Data analysis – using computers for presentationNoonapau
The document discusses using computer software for data analysis. It provides examples of different types of software including word processors, code-and-retrieve programs, and conceptual network builders. It emphasizes that the researcher should choose software based on their methodology and the type and amount of data, rather than which software is considered "best." The document also summarizes several research articles that used different software programs like MS Word, NVivo, and Qualrus to analyze qualitative data.
The document discusses using computer software to analyze qualitative data, describing different types of analysis software and their functions. It also provides examples of research studies that used various computer-assisted qualitative data analysis software packages like MS Word, NVivo, and NUD*IST to code and analyze interview transcripts, field notes, and other qualitative data sources. The document emphasizes that the choice of software depends on the researcher's methodology, data types and amount, and analysis approach.
NagaRaju Addanki is a software developer with over 7 years of experience seeking new project opportunities. He has extensive experience developing web applications using Microsoft technologies like ASP.NET, C#, and SQL Server. Currently he works as a module lead at Value Labs in Hyderabad, India where he supports payroll projects and applications. His background includes developing academic, e-commerce, and database applications for clients.
Qualitative data analysis software's By Iqbal RanaIqbal Rana
this ppt is the brief introduction of Qualitative data analysis software. it will be helpful for beginner researchers to opt a relevant data analysis software for their research
NagaRaju Addanki is a senior technical lead with over 8 years of experience developing web applications. He has extensive experience using Microsoft technologies like ASP.NET, C#, and SQL Server. Currently he works at Value Labs as a technical lead on payroll projects. He is seeking new project opportunities in software development.
This posting is for a senior Java developer who will design, build, test, operate and support web applications. The ideal candidate has 8+ years of experience developing Java web applications using technologies like Spring, Hibernate, Oracle databases and designing systems to meet business requirements. Responsibilities include analyzing requirements, designing and implementing solutions, and providing support for applications.
Akshay Shaha is a technical lead with 5 years of experience in data warehousing and business intelligence projects. He has expertise in Teradata, Informatica, and SQL. Shaha currently works as a consultant for a large healthcare client at Deloitte, where he leads a team developing software to analyze and report on healthcare provider costs and services. Previously he has worked on other data-focused projects in healthcare and banking. Shaha holds a Bachelor's degree in Information Technology.
Nasim Razavi is a software and database developer with over 15 years of experience in software design, development, testing, and maintenance. He has a Master's degree in Computer Science from York University and a Bachelor's degree in Computer Engineering. His technical skills include languages like C/C++, Java, and databases like DB2 and SQL Server. He has experience in all phases of the development lifecycle including analyzing requirements, designing interfaces, writing code, testing, and supporting users. He is currently a teaching and research assistant at York University.
Details
For September, DataScience Sg is starting a new series specially for the undergrads. The series aims to showcase undergrads and fresh grads project work.
The series is meant to encourage youths in joining the data science & artificial intelligence career. And for the employers to come in and recruit talents for your companies.
In this inaugural meetup for the series, we have the following youths to share about their work and project and how their projects helped them in their current career.
DSSG strongly encourage current undergrads and fresh grads to join us in this series. Its still open to the general community!
Details:
Ivan is currently a Data Scientist at Tech In Asia (TIA), with experience in developing recommender systems, customer churn prediction, network analysis and driving BI solutions through data visualization and analytics. He graduated with a Bachelor of Science (Informations Systems) and Major in Marketing Analytics from SMU in 2018.
Ivan will be sharing about his Final Year Project when he was an undergrad at SMU — KDDLabs, a web-based data mining application while explaining the team’s motivations, challenges and key takeaways. In addition, he will also be talking about his first data product at TIA, developing recommender systems to help better connect jobseekers with employers and vice versa.
LinkedIn: https://www.linkedin.com/in/yongsiang/
FYP: http://smu.sg/kddlabs
This document contains the resume of TMNK SHESHA SAI, an experienced software testing professional. It summarizes his career, qualifications, skills, and project experience. He has over 3 years of experience testing software in the healthcare domain. Some of the key projects he has worked on include testing analytics platforms, member portals, and applications involving ETL processes and data integration. He is proficient in manual testing, database testing, test automation, and defect tracking using tools like TFS, Mantis, and TeamTrack.
Meha Ghadge is a database expert with over 8 years of experience in the IT industry. She has extensive experience designing, coding, testing and optimizing database performance. Currently she is a technical lead for Moody's Investor Services where she is responsible for analyzing database structures, mapping data fields, and documenting business rules and processes. She has led multiple projects involving database development, performance tuning, and coordinating with backend and frontend teams.
- Sarah Bennett is a Technology Integrity Analyst at National Indemnity Company with over 3 years of experience testing .NET and web applications using skills like manual software testing, SQL Server, Agile environments, and technical writing.
- She acts as a testing lead on product development teams, researches user needs, and develops comprehensive testing strategies.
- Prior to her current role, she was a Technology Integrity Specialist and help desk attendant at National Indemnity, and conducted independent research as an NSF intern studying iron speciation in water systems.
- Her education includes a BA in Mathematics from the University of Colorado at Boulder with a certification in Actuarial Studies and an unaccredited BA
Debasish Mahapatra is a seasoned business analyst and project manager with over 3 years of experience in the IT industry. He has extensive skills in project management, requirements gathering, documentation, and testing. He is proficient in technologies like Java, J2EE, Oracle, and Agile methodologies. Debasish aims to contribute his analytical abilities and expertise in managing software development projects to deliver business solutions.
The measurement of student performance- The futuristic approach focuses on the development of the child. Therefore, teachers try to maintain a record of performance for each child performance. And with this, they make the child improvement and prepare them to compete and the global level.
The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation
in analytical dashboards is a ‘drill-down’, which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.
Directed versus undirected network analysis of student essaysRoy Clariana
IWALS 2018
6th International Workshop on Advanced Learning Sciences
Perspectives on the Learner: Cognition, Brain, and Education
University of Pittsburgh, USA JUNE 6-8, 2018
Using a keyword extraction pipeline to understand concepts in future work sec...Kai Li
This document describes a study that uses natural language processing and text mining techniques to identify future work statements in scientific papers and extract keywords from those statements. The researchers developed a multi-step pipeline to first identify the future work section, then select future work sentences within that section. They used rules and algorithms to identify sentences discussing future work. Keywords were then extracted from the selected sentences using the RAKE algorithm. An analysis found that 31.4% of papers contained future work statements, with medical science papers having the highest overlap between future work and title-abstract keywords. The researchers hope this work is a first step toward predicting future research topics.
This Tutorial contains 2 Set of Papers for each Assignment
CIS 532 Week 2 Assignment 1 Request for Proposal Response (2 Papers)
CIS 532 Week 3 Case Study 1 Harriet’s Fruit and Chocolate Company (2 Papers)
Workshop 2 using nvivo 12 for qualitative data analysisDr. Yaar Muhammad
This document provides an overview of using NVivo 12 for qualitative data analysis. It discusses the seven key stages of qualitative analysis: 1) importing data, 2) coding data, 3) creating framework matrices, 4) reporting findings. It describes how to import various file types into NVivo and code data using both first and second cycle coding methods. Framework matrices allow for analyzing patterns across cases. Well supported assertions should be used to report the findings of the qualitative analysis.
For more course tutorials visit
www.tutorialrank.com
This Tutorial contains 2 Set of Papers for each Assignment
CIS 532 Week 2 Assignment 1 Request for Proposal Response (2 Papers)
CIS 532 Week 3 Case Study 1 Harriet’s Fruit and Chocolate Company (2 Papers)
Haystack 2018 - Algorithmic Extraction of Keywords Concepts and VocabulariesMax Irwin
Presentation as given to the Haystack Conference, which outlines research and techniques for automatic extraction of keywords, concepts, and vocabularies from text corpora.
Group X analyzed data using computer software. They discussed several types of software for analyzing qualitative data, including those for coding text, developing theories, and building conceptual networks. The functions to look for include coding, memoing, searching, and displaying data. There is no single best software; the researcher must consider their data, approach, and needs. The document provided examples of research articles that used different software like MS Word, NVivo, and Qualrus to analyze qualitative data.
Data analysis – using computers for presentationNoonapau
The document discusses using computer software for data analysis. It provides examples of different types of software including word processors, code-and-retrieve programs, and conceptual network builders. It emphasizes that the researcher should choose software based on their methodology and the type and amount of data, rather than which software is considered "best." The document also summarizes several research articles that used different software programs like MS Word, NVivo, and Qualrus to analyze qualitative data.
The document discusses using computer software to analyze qualitative data, describing different types of analysis software and their functions. It also provides examples of research studies that used various computer-assisted qualitative data analysis software packages like MS Word, NVivo, and NUD*IST to code and analyze interview transcripts, field notes, and other qualitative data sources. The document emphasizes that the choice of software depends on the researcher's methodology, data types and amount, and analysis approach.
NagaRaju Addanki is a software developer with over 7 years of experience seeking new project opportunities. He has extensive experience developing web applications using Microsoft technologies like ASP.NET, C#, and SQL Server. Currently he works as a module lead at Value Labs in Hyderabad, India where he supports payroll projects and applications. His background includes developing academic, e-commerce, and database applications for clients.
Qualitative data analysis software's By Iqbal RanaIqbal Rana
this ppt is the brief introduction of Qualitative data analysis software. it will be helpful for beginner researchers to opt a relevant data analysis software for their research
NagaRaju Addanki is a senior technical lead with over 8 years of experience developing web applications. He has extensive experience using Microsoft technologies like ASP.NET, C#, and SQL Server. Currently he works at Value Labs as a technical lead on payroll projects. He is seeking new project opportunities in software development.
This posting is for a senior Java developer who will design, build, test, operate and support web applications. The ideal candidate has 8+ years of experience developing Java web applications using technologies like Spring, Hibernate, Oracle databases and designing systems to meet business requirements. Responsibilities include analyzing requirements, designing and implementing solutions, and providing support for applications.
Akshay Shaha is a technical lead with 5 years of experience in data warehousing and business intelligence projects. He has expertise in Teradata, Informatica, and SQL. Shaha currently works as a consultant for a large healthcare client at Deloitte, where he leads a team developing software to analyze and report on healthcare provider costs and services. Previously he has worked on other data-focused projects in healthcare and banking. Shaha holds a Bachelor's degree in Information Technology.
Nasim Razavi is a software and database developer with over 15 years of experience in software design, development, testing, and maintenance. He has a Master's degree in Computer Science from York University and a Bachelor's degree in Computer Engineering. His technical skills include languages like C/C++, Java, and databases like DB2 and SQL Server. He has experience in all phases of the development lifecycle including analyzing requirements, designing interfaces, writing code, testing, and supporting users. He is currently a teaching and research assistant at York University.
Details
For September, DataScience Sg is starting a new series specially for the undergrads. The series aims to showcase undergrads and fresh grads project work.
The series is meant to encourage youths in joining the data science & artificial intelligence career. And for the employers to come in and recruit talents for your companies.
In this inaugural meetup for the series, we have the following youths to share about their work and project and how their projects helped them in their current career.
DSSG strongly encourage current undergrads and fresh grads to join us in this series. Its still open to the general community!
Details:
Ivan is currently a Data Scientist at Tech In Asia (TIA), with experience in developing recommender systems, customer churn prediction, network analysis and driving BI solutions through data visualization and analytics. He graduated with a Bachelor of Science (Informations Systems) and Major in Marketing Analytics from SMU in 2018.
Ivan will be sharing about his Final Year Project when he was an undergrad at SMU — KDDLabs, a web-based data mining application while explaining the team’s motivations, challenges and key takeaways. In addition, he will also be talking about his first data product at TIA, developing recommender systems to help better connect jobseekers with employers and vice versa.
LinkedIn: https://www.linkedin.com/in/yongsiang/
FYP: http://smu.sg/kddlabs
This document contains the resume of TMNK SHESHA SAI, an experienced software testing professional. It summarizes his career, qualifications, skills, and project experience. He has over 3 years of experience testing software in the healthcare domain. Some of the key projects he has worked on include testing analytics platforms, member portals, and applications involving ETL processes and data integration. He is proficient in manual testing, database testing, test automation, and defect tracking using tools like TFS, Mantis, and TeamTrack.
Meha Ghadge is a database expert with over 8 years of experience in the IT industry. She has extensive experience designing, coding, testing and optimizing database performance. Currently she is a technical lead for Moody's Investor Services where she is responsible for analyzing database structures, mapping data fields, and documenting business rules and processes. She has led multiple projects involving database development, performance tuning, and coordinating with backend and frontend teams.
- Sarah Bennett is a Technology Integrity Analyst at National Indemnity Company with over 3 years of experience testing .NET and web applications using skills like manual software testing, SQL Server, Agile environments, and technical writing.
- She acts as a testing lead on product development teams, researches user needs, and develops comprehensive testing strategies.
- Prior to her current role, she was a Technology Integrity Specialist and help desk attendant at National Indemnity, and conducted independent research as an NSF intern studying iron speciation in water systems.
- Her education includes a BA in Mathematics from the University of Colorado at Boulder with a certification in Actuarial Studies and an unaccredited BA
Debasish Mahapatra is a seasoned business analyst and project manager with over 3 years of experience in the IT industry. He has extensive skills in project management, requirements gathering, documentation, and testing. He is proficient in technologies like Java, J2EE, Oracle, and Agile methodologies. Debasish aims to contribute his analytical abilities and expertise in managing software development projects to deliver business solutions.
The measurement of student performance- The futuristic approach focuses on the development of the child. Therefore, teachers try to maintain a record of performance for each child performance. And with this, they make the child improvement and prepare them to compete and the global level.
• Excellent analytical and problem solving skills.
• Excellent communication skills.
• Quick Learner, Self-Motivated and team player traits.
• Ability to mentor and educate peers whenever needed for the greater good of the team as a whole.
Lav Kumar Shukla is seeking a position that allows him to apply his skills and experience in an organization with a knowledge-focused culture. He has over 1 year of experience as an Hadoop Developer using tools like HDFS, MapReduce, Apache Pig, Hive, SQOOP, and HBase. His most recent role involved analyzing book data from the Book-Crossing dataset using these Hadoop technologies. He is proficient in languages like Java, Python, and technologies including HDFS, MySQL, Eclipse and Linux. Lav Kumar holds a Bachelor's degree in Information Technology.
Highlights and summary of long-running programmatic research on data science; practices, roles, tools, skills, organization models, workflow, outlook, etc. Profiles and persona definition for data scientist model. Landscape of org models for data science and drivers for capability planning. Secondary research materials.
Subnetting Project.You are a consultant for being brought in b.docxmattinsonjanel
Subnetting Project.
You are a consultant for being brought in by XUMUC to assist with a merger with another company.
Background:
XUMUC is has the WAN links in place to the new locations in the Houston Region. XUMUC currently has 2 other Regions San Francisco and Denver. Originally, XUMUC was only in one region (San Francisco). The previous consultant did a poor job with the integration resulting in a poor IP address scheme as a result routing tables at the summarization points and at the San Francisco Campus are very large. In addition, no VLAN structure was developed to isolate broadcast traffic. There are 4 main departments in XUMC: sales, finance, human resources, and research and development. Also, there has been some concern that the WAN transport was not able to accommodate the network traffic. Finally, all addresses in the network are statically assigned resulting in high administration overhead when changes are needed XUMC would like this changed to lower administrative overhead.
IP ADDRESSING TABLE
Location
Number of IP Addresses Required
Address Block Assigned
San Francisco
1290
172.16.0.0-172.16.7.255/21
Denver Region
Denver Campus
441
Remote Office 1
28
Remote Office 2
35
Houston Region
Houston Campus
329
Remote Office 3
21
Deliverables:
Provide a document that addresses all issues described above.
The document should contain:
· Cover page
· Index Page
· Executive summary
· Technical details (including any assumptions)
· Details that address all issues described above
· Completed IP addressing table (including summarized routes for the Denver and Houston regions),
· Updated network diagram and a conclusion.
· Conclusion
· Reference page
XMUMC Network Diagram
SYSTEMS ANALYSIS AND DESIGN - TOPICS
Theories, Tools and Practices related to Systems Analysis and Design (SA&D) including (but not
limited to) the following topics:
Evolution of Systems Analysis and Design
Empirical Studies of SA&D methods
Principles and Methodologies
Initiating and Planning Systems Development Projects
Development Life Cycle
Soft Systems Methodology
Joint Application Design
Structured and Object Oriented SAD
Goal oriented SAD
Information Engineering
Expert Analysis and design
System Life Cycle
Specification Development
Requirement Discovery
Requirement Analysis Paradigm
Economics of SAD
Feasibility Analysis
Analysis Methods of Current Systems
Information Gathering Methods and Tools
Logical and Physical Design methods
Database Design
Data Design Methods
File Design
Business Process Modelling
Alternative Design Strategies
Prototyping
Decision Analysis
Risk Analysis
Knowledge based Analysis and Design
Model Driven Analysis
Accelerated Systems Analysis
Component Design
Comparative Study and Evaluation of SA&D methods
Implementation and Testing
Design of Testing
Testing of Design
Human Interface Design
Hardware Interface Design
Roles in Systems Analysis and Desi ...
Bikash Roy has over 12 years of experience in software development using Microsoft technologies. He is currently a Senior Manager at Healthcare Ideas Limited where he is responsible for all software development activities including monitoring teams, planning projects, and ensuring delivery. Previously he has held positions as a Project Manager, Manager, Team Leader and Software Engineer/Programmer at various companies. He has expertise in ASP.NET, C#, SQL Server, JavaScript and other technologies.
Aspen University EDD830 Module 5 Discussion Questioneckchela
This is EDD830 Module 5 Discussion 1, at Aspen University. The post is written in APA format with references: What is the difference between criticism and coaching feedback?
Why is leadership a key issue in management? eckchela
This is Aspen University (EdD) Module 1 Assignment: Leadership Theory. It is written in APA format, and it has been graded by Dr. Campbell (A): Orlanda - From the first section to the last, I can tell that you truly do understand the importance of scholarly writing and citing. Your well referenced and organized presentation quickly allowed me to enjoy reading your various points that you made concerning managerial and leadership options in regards to effective relationships within an organization. I appreciate your introduction and concluding remarks that helped to tie the assignment together.
By Day 6
Respond to at least one of your colleagues’ posts and (1) explain the multicultural and/or diversity considerations needed, as they relate to the development of the milestone. This discussion is written in APA format and has been graded (A).
This is Walden Univesity DPSY 6121/DPSY 8121 Week 11 Discussion:
Post a description of one event or activity when individuals or groups benefited from using digital technologies for positive social change. Include at least one scholarly source.
DPSY 6121-8121 Week 10 Final Project: Mitigation Planeckchela
This is Walden University course (DPSY 6121/8121) Week 10 Assignment: Document (PowerPoint presentation is part 2). It is written in APA format, includes references, and has been graded by Dr. Essel (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
This is Walden University (DPS Y 5121-1 and 8121-1) Week 9 Discussion 2. It is written in APA, has references, and graded by Dr. Essel (A). Most education communities submit scholarly writings to Turnitin; so, remember to paraphrase.
This is Walden University course (DPSY 6121/8121) Week 7 Discussion. It is written in APA format, includes references, and has been graded by an instructor (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
This is Walden University course (DPSY 5111-6121-8121) Week 6 Discussion. It is written in APA format, includes references, and has been graded by an instructor (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
Digital technology can impact the development of self-esteem in adolescents in both positive and negative ways. Positively, social media allows teens to share their lives which can improve self-esteem through social comparison. However, comparison on social media can also cause teens to feel inadequate. Excessive social media use is linked to addiction which impacts self-esteem. While social media gives shy teens a way to connect, low self-esteem users may experience further drops in self-esteem from online interactions. Research shows self-esteem is lowest in adolescence and develops over the lifespan with both digital impacts and traditional influences.
EL-7010 Week 1 Assignment: Online Learning for the K-12 Studentseckchela
This is a North Central University PowerPoint presentation (EL 7010) Week 1 Assignment. It is written in APA format, has been graded by an instructor(A), and includes references. Most higher-education assignments are submitted to turnitin, so remember to paraphrase. Let us begin.
DPSY 5111-6111 Week 3 Assignment: Final Project: Mitigation Planeckchela
This is Walden University course (DPSY 5111-6111) Week 3 Assignment. It is written in APA format, includes references, and has been graded by Dr. Essel (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
This is a North Central University PowerPoint presentation (EDR 8204-3). It is written in APA format, has been graded by an instructor(A), and includes references. Most education communities submit assignments to turnitin, so remember to paraphrase.
This is Walden University course (DPSY 6111/8111) Week 5 Assignment: Cognitive Development. It is written in APA format, includes references, and has been graded by an instructor (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
This is Walden University course (DPSY 6111/8111) Assignment 10. It is written in APA format, includes references, and has been graded by an instructor (A). Most higher-education assignments are submitted to turnitin, remember to paraphrase. Let us begin.
By Day 4
Post a brief summary of the article and explain how stereotype threat or stereotype lift might have influenced your own academic performance in school. Explain how the theory you chose (i.e., social role theory or psychosocial theory) relates to the stereotype threat or lift you described.
By Day 4
Based on the scenario, explain the moral dilemma in light of Kohlberg’s theory and posit how Tony might decide to resolve his dilemma. Explain how the reasoning behind the decision might be different if following Gilligan’s or another moral development perspective and why. Compare the reasoning behind what Tony might do if he were in high school versus if he were in college.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Information and Communication Technology in Education
EDR8204-7
1. OHAYNESEDR8204-7 1
NORTHCENTRAL UNIVERSITY
ASSIGNMENT COVER SHEET
Student: Orlanda Haynes Date: 04/22/2018
THIS FORM MUST BE COMPLETELY FILLED IN
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sheet. This will become the first page of your assignment. In addition, your assignment header
should include your last name, first initial, course code, dash, and assignment number. This
should be left justified, with the page number right justified. For example:
DoeJXXX0000-1 1
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includes all assignments, exams, term papers, and other projects required by your instructor.
Knowingly submitting another person’s work as your own, without properly citing the source of
the work, is considered plagiarism. This will result in an unsatisfactory grade for the work
submitted or for the entire course. It may also result in academic dismissal from the University.
EDR8204-6
Week 7 Assignment: Explore Qualitative Analysis Software
Instructor: Dr. Kanyongo
Faculty Only
Hi Orlanda, this assignment required you to develop a brief critique (rather than a simple
description) of how well each of the qualitative data analysis programs supports the
qualitative data analysis process. Please note that for your qualitative study you will
be required to use one of these programs to analyze your data. Therefore,
developing an understanding of these qualitative software programs is essential.
Overall you did a good job in your critiquing of the programs, therefore you obtained
100% on this assignment. I have no doubt that you will be able to apply these
2. OHAYNESEDR8204-7 2
programs to your qualitative data analysis. The grade justification is provided in the
narrative below:
Completes all required parts of the assignment, demonstrates deep understanding of materials,
uses very clear and effective expression appropriate to scholarly writing, and has very
few or no errors in grammar, mechanics, and APA formatting.
Please let me know if you have any questions on any concept covered in this assignment.
3. OHAYNESEDR8204-7 3
Week 7 - Assignment
Explore Qualitative Analysis Software
The purpose of this assignment is to create a chart of at least three qualitative analysis
software (QAS) programs. Chart headings include (a) coding tools, (b) content analysis, (c)
discourse analysis, and (d) query, writing, transcription, and annotation. An overview of some
advantages and disadvantages are noted. QAS are tools for conducting analysis of transcriptions,
texts, images, social media posts, emails, datasets, coding, focus group data, and grounded theory
methods, to name a few (https://www.predictiveanalyticstoday.com/top-qualitative-data-
analysis-software/). Although there are many options to choose from, MAXQDA, NVivo,
QDA Miner and QDA Miner Lite are the most popular among education communities
(https://www.thoughtco.com/analyze-qualitative-data-software-3026538; https://gse-
it.stanford.edu/workshops/comparison-qualitative-analysis-software). These tools offer
numerous benefits including time-saving, management of large datasets, procedural flexibility,
and increased validity of data analysis (https://www.predictiveanalyticstoday.com/top-
qualitative-data-analysis-software/). That said, let us take a look at the following chart.
Figure 1
Qualitative Analysis Software
QAS MAXQDA NVivo QDA Miner and QDA Miner
Lite
Supported OS Windows
and Mac
Win XP, XPT or
later
Windows and Mac
Coding Tools Text and Images Only
Content Analysis Text and Images Only
Discourse Analysis Text and Images Only
Query, Writing,
Transcription, and
annotation
Text and Images Only
Text Interpretation
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Mapping or
Networking
Linking Ability
Qualitative and Mixed
Methods Research.
Text and Images Only,
integrates with SimStat
Costly Free (QDA Miner Only)
NVivo
it can be costly to purchase as an individual, but people working
in education get a discount, and students can buy a 12-month
license for about $100.
the strength of NVivo lies in its high compatibility to research
designs. The software is not methodological-specific, it works well
with wide range of qualitative research designs and data analysis
methods such as discourse analysis, grounded theory,
conversation analysis, ethnography, literature reviews,
phenomenology, and mixed methods.
sophisticated QDA program
-support text, PDF, survey,
audio, video and graphical
files
-lots of built-in functions for
coding, retrieving, analyzing,
visualizing and exporting
-responsive to enquiries and
suggestions
-face-to-face training
workshops are regularly
scheduled
-allow for team work
Disadvantage
-some functions are not
intuitive and therefore take
time to learn by reading the
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manual or by viewing the
online tutorials
-some query functions are
not intuitive therefore take
time to learn
Different layout than Windows version
-Some functions not yet available for Mac version
comparison_of_five_qualitative_data_analysis_qda_software_-
_version_2_-_neville_li.pdf
QDA Miner and QDA Miner Lite
Unlike Nvivo, QDA Miner and its free version-- work stricly with
text documents and images.
As such, they offer fewer functions than Nvivo and others listed
below, but they are fantastic tools for researchers focusing on
analysis of text or images.
, QDA Miner can be integrated with SimStat for quantitative
analysis, which makes it a great mixed-methods data analysis
software tool. Qualitative researchers use QDA Miner to code,
memo, and analyze textual data and images.
It offers a range of features for coding and linking sections of data
together, and also for linking data to other files and webpages.
The program offers geo-tagging and time-tagging of text segments
and graphicareas, and allows users to import directly from web
survey platforms, social media, email providers, and software for
managing references.
QDA Miner is costly but is much more affordable for people in
academia. The free version, QDA Miner Lite, is a great basic
tool for text and image analysis. It does not have all the features as
the pay-version, but can get the coding job done and allow for
useful analysis.
are used by more than 2,000 institutions including universities, governments,
NGO’s and businesses.Provalis Research textanalytics softwareis a collection of
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tools that allow users to explore, analyzeand relate both structured and
unstructured data. The main tools are QDA Miner, WordStatand SimStat. Provalis
QDA Miner
sophisticated QDA program
-support text, PDF, survey,
audio, video and graphical
files
-lots of built-in functions for
coding, retrieving, analyzing,
visualizing and exporting
-online automated training
tutorials are available
disadvantage
lack of training
opportunities; looks like
users have to learn this
product on their own
-may not be as quick in
product development as
other QDA products
(https://www.predictiveanalyticstoday.com/top-qualitative-data-analysis-software/).
What does MAXQDAdo?
-sophisticated QDAprogram
-supporttext, PDF,survey,
audio,video and graphical
files
-lots of built-in functions for
coding,retrieving,analyzing,
visualizing and exporting
-program is very easy to
learn and intuitive
-the built-in query tools,
mixed methods tabs and
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The only software thathas the same functions as its
Windows version
-The company,even thoughbasedin Germany,is very
responsive to providefeedbackfor questions aboutdifferent
versions
comparison_of_five_qualitative_data_analysis_qda_software_-
_version_2_-_neville_li.pdf
disadvantage
MAXDictio tab (at an extra
cost) are very easy to learn
and well-suited for
qualitative and mixed
methods research
-MAXReaderis available
FREE of chargefor people
who do not have the
software to view the
documents
-lots of free online tutorials
available on website
-allow for team work
not many users in Canada or
the United States at the
momentcomparedto
ATLAS.ti and NVivo;
thereforelack a community
of users
MAXQDA helps you to collect,organize, analyze, visualize and publish
data from qualitative, quantitative and mixed methods
research. Over 25 years of experience and constant developmenthave
made MAXQDA one of the most comprehensive analysis
programs in the industry. This page will give you an idea of the full scope of
MAXQDA’s features.
MAXQDA is also the world’s first QDA program to include additional
features for focus group discussions.Differentspeakers can be
8. OHAYNESEDR8204-7 8
automatically recognized,so you can easily compare their contributions,
analyze each speaker on her own, and visualize them in a
variety of ways.
The great thing about MAXQDA is that it offers several versions
from basic to advanced functionality that offer a range of options,
including text analysis, data collected through a variety of
qualitative methods, transcription and coding of audio and video
files, quantitative text analysis, integration of demographic data,
and data visualization and theory testing. It functions much like
Nvivo and Atlas.ti (described below).
(https://www.maxqda.com).
comparison_of_five_qualitative_data_analysis_qda_software_-_version_2_-_neville_li.pdf
In brief,
In general, the question of which QDA software to use has been asked several times on
ResearchGate, and answer is always that it depends on your personal preferences. Pretty much
the same features are available from all of the major packages -- MAXQDA, NVivo, ATLAS.ti,
and Dedoose. So, a lot of the decision comes down to "look and feel" -- i.e., what works best for
you.
Fortunately, these programs all offer free trial versions and online video tutorials, so you can try
them out first.
Zamawe, F. C. (2015). The Implication of Using NVivo Software in Qualitative Data Analysis:
Evidence-Based Reflections. Malawi Medical Journal, 27(1), 13–15. Retrieved from
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4478399/