Predictive Hr Analytics, Text Mining & Organizational Network Analysis With Excel

A lot of organizational data is often untapped unstructured data in the form of text & numbers. You don't need to spend months learning R programming & you don't need to buy expensive SPSS statistical software. This is the only book that teaches you how to use Microsoft Excel for Predictive HR Analytics, Text Mining & Organizational Network Analysis (ONA) with step-by-step print-screen instructions: 1) Predictive HR Analytics: Use Excel's Statistical Analysis tools (Decision trees, Correlation, Multiple & Logistic Regression) to run Predictive HR Analytics. E.g. an employee is predicted to have a 60% probability of getting into accidents, if he is age 25, worked 1 year in the company & took 6 days sick leave. An employee is predicted to get rated "7" for Customer Service, if the training program that he attended has a training evaluation score of "8". An employee is predicted to resign if she is age 23, worked for 2 years, and takes 60 minutes to commute to work. 2) Organizational Network Analysis (ONA): Run ONA using Excel's network analysis tool. Learn how to convert an employee's organizational network into a score & then predict if they will be a high-potential (HiPo). E.g. an employee is predicted to be a HiPo with performance rating of "9", if his "Social Network Size" is "16", "Social Network Diversity Index" is "3" & "Competency Score" is "8". 3) Text Mining, Sentiment Analysis & Word Clouds: Mine text from social network posts, employee engagement surveys & Glassdoor comments, then run Sentiment Analysis using Excel & visualize the insights with "Word Clouds". Learn how to predict a company's average employee attrition rate based on its sentiment. E.g. a company's average employee attrition rate is predicted to be 8%, if unemployment rate is 3%, GDP growth is 2%, Glassdoor public sentiment rating is "5", and engagement score is "7".

• | Author: Dpg, Mong Shen Ng
• | Publisher: Independently published
• | Publication Date: Jun 30, 2019
• | Number of Pages: 500 pages
• | Language: English
• | Binding: Paperback
• | ISBN-10: 107722690X
• | ISBN-13: 9781077226906