FDA Collection:http://hdl.handle.net/2451/622362026-04-14T07:10:16Z2026-04-14T07:10:16ZCPM Assessment Knowledge Assessment for Management Skills Based on AMA Book of KnowledgeCai, Minghttp://hdl.handle.net/2451/628062021-07-13T13:28:34Z2021-06-17T00:00:00ZTitle: CPM Assessment Knowledge Assessment for Management Skills Based on AMA Book of Knowledge
Authors: Cai, Ming
Abstract: This project is to establish a test bank and evaluate its efficacy and effectiveness to improve the management knowledge, skills, and competency of students graduating from a STEM program. This project is to make students better prepare for AMA’s Professional Management Certification exam. This project shows that many students of management-related majors graduating from New York University do not have the adequate management capacity and skills. A test bank is a useful tool to help students to have a better understanding of their learning outcome and progress. This test bank can help faculty and course teachers to modify and improve their curriculum.
Background – While higher education institutions are to equip students with the hard and soft skills to allow them to have a better preparation for the highly competitive labor market, many higher education institutions fail to do so, so many employees complain that higher education institutions should do more to improve the knowledge, skills, and competencies of students graduating from colleges or universities. The AMA Certified Professional in Management Exam is an important certification exam for managers or prospective managers. Although AMA provides lots of resources for people to learn, many of them do not know if they are competent enough to pass this certification exam.
Research questions -- We asked several questions for this research. 1) if students of management-related majors graduating from New York University have the adequate management capacity and skills; 2) what could schools do to help these students to improve their management capacity and skills to pass a management certification exam; 3) if a test bank is a useful tool to help students to have a better understanding of their learning outcome and progress, according to which they could improve their learning outcomes; 4) if this test bank can help faculty and course teachers to modify and improve their curriculum.
Methods-- This project invites students participating in the Applied Projects capstone course in Spring 2021 and 16 of them participated and completed this project.
Index Terms – hard skills, soft skills, management competencies, certification2021-06-17T00:00:00ZOccupation AnalyzerWu, Yuchaohttp://hdl.handle.net/2451/628052021-07-13T13:25:13Z2021-06-17T00:00:00ZTitle: Occupation Analyzer
Authors: Wu, Yuchao
Abstract: Abstract
The purpose of this project is to develop a text analytical tool in R that can help student job seekers match their resumes against jobs by a mutual similarity scoring to standard occupations as defined by the BLS O*NET database. Part of the terms of job evaluation for job seekers is to discover their educational preparation for a particular desirable job. The tool scores the resume against a group of jobs and presents the user with the top-scoring jobs to select a target. The resulting top target job table is scored against the occupations. A cosine similarity score is computed between the top job/occupation scores and the resume/occupation. The ranked jobs are then presented to the user as best matched to their resume. Users can interact with this tool on a website that relied on R Shiny. The website contains an occupation database and requires users to upload their resumes and a list of jobs as inputs. The outputs include detailed information about the top 15 jobs and the bottom five jobs. The project expands on existing preliminary work done in Python. The data preprocessing part includes making all text lower cases, removing punctuations, special characters, numbers, English common stop words, extra white spaces, and stemming. It uses the term frequency-inverse document frequency (TF-IDF) algorithm and cosine similarity to measure the similarity among the resume, jobs, and occupations. The final results meet sponsors’ expectations and show significant differences between previous work done in Python. Using n-grams, sliding windows, and named-entity extraction are possible methods to improve the tool’s performance. I will conduct further research this summer to enhance the accuracy. All project files are stored in a GitHub repository.2021-06-17T00:00:00ZIndustry Code Analyzer – NAICS Code Discovery For Startups in RXu, Yin (Fein)http://hdl.handle.net/2451/628042021-07-13T13:21:27Z2021-06-17T00:00:00ZTitle: Industry Code Analyzer – NAICS Code Discovery For Startups in R
Authors: Xu, Yin (Fein)
Abstract: NAICS Code is a classification adopted by the North American Industry Classification System. Federal Statistical Agencies use the code to establish a North American standard on collecting and analyzing statistical data related to the U.S. Economy. However, NAICS is a self-assigned system. Business owners or users have to select the code that best describes their primary business activities. It is time-consuming and inefficient for business owners or users to manually use keyword search provided by the NAICS Code system, bounced back and forth among search pages and homepage. Therefore, this project aims to help startups or users of the NAICS Code system find correct and corresponding industry codes based on their business. The consultant has earlier work in building a NAICS industry code search tool in Python. The project expanded on existing preliminary work done in Python. The consultant of this project developed a tool in R to search the NAICS industry code database more intelligently. Given a business description as a text file, the industry code analyzer tool searches the NAICS industry code database to identify the industry classification corresponding to the users' uploaded business descriptions. The industry code analyzer tool uses TF-IDF text similarity scoring, returns a ranked list of industry codes, and presents the top 5 codes and descriptions to the user for selection and download. This project carried on additional experiments to define the tool capabilities and produced a user interface in Shiny for easier use of the tool. The shiny app regarding industry code analyzer served as an efficient search tool to find the correct industry code and benefit both professional and academic uses. The accuracy rate of this industry code tool reaches 79%, compared to the result generated from preliminary work in Python and code manually found by business owners. Further development regarding TF-IDF decomposition dimensionality reduction is suggested to adopt in the next phase to enhance accuracy and reducing process time in text analysis.2021-06-17T00:00:00ZSimi Bot (Text Similarity Analyzer) - Best PaperChen, Wukun (Eric)http://hdl.handle.net/2451/628032021-07-13T13:30:47Z2021-06-17T00:00:00ZTitle: Simi Bot (Text Similarity Analyzer) - Best Paper
Authors: Chen, Wukun (Eric)
Abstract: Abstract
The purpose of this project is to develop an application to perform TF-IDF text similarity scoring analysis for NYU School of Professional Studies and the Management and Systems program (MASY). This application is programmed in the R programming language and hosted on the shinyapp.io server. This text data mining application is featuring topic clustering, keyword extraction, machine learning, cloud computing, and Shiny-based user experience. This project allows users to customize unsupervised machine learning hyperparameters and upload files locally. After uploading a .txt file (comparison source) and a .csv file (comparison target), users need to choose the number of clusters for the text cluster analysis (from 2 to 20), the number of most frequent words to display for each text cluster (from 2 to 20), and the level of word combinations (from 1 to 3). When a user clicks the “Analyze Data” button after all the hyperparameters are set, this application will generate two data tables to indicate similarity scores, cluster group, size of each cluster group, and the keyword in each cluster group.
The underlying algorithms of this program are as following: cleanse the text source and target, drop all non-alphabetical characters, eliminate multi-space, and lemmatize all the words; apply TF-IDF transformation, compute similarity-score against the source to each target; cluster with hierarchical method; calculate the mean similarity scoring by the group to determine the cluster of the max mean; output data table with cluster group, size of each cluster group, and the keyword in each cluster group.
With this new tool, students studying data analysis and machine learning would have an easy-to-use R tool to perform TF-IDF text similarity scoring analysis, which works for both Windows-based PCs and Apple Macs. Scenarios that we can put into use include matching resumes to occupations, matching a syllabus to occupations, matching resumes to program syllabi to discover gaps, and recommend courses. Templates, samples, and comprehensive tutorials are provided in the application.
Description: Best in Showcase Paper2021-06-17T00:00:00Z