Informações sobre o curso: People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. They’ll explain how data and sophisticated analysis is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development, so that you can position yourself as a strategic partner in your company’s talent management decisions. Organizations flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.
Desenvolvido por: Universidade da Pensilvânia
Ministrado por: Cade Massey, Practice Professor
The Wharton School
Ministrado por: Martine Haas, Associate Professor of Management
The Wharton School
Ministrado por: Matthew Bidwell, Associate Professor of Management
The Wharton School
| Informações básicas | Curso 3 de 5 no Análises Empresariais Specialization |
| Compromisso | 4 semanas de estudo, 1-2 horas/semana |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
Programa
SEMANA 1
Introduction to People Analytics, and Performance Evaluation
In this module, you'll meet Professors Massey, Bidwell, and Haas, cover the structore and scope of the course, and dive into the first topic: Performance Evaluation. Performance evaluation plays an influential role in our work lives, whether it is used to reward or punish and/or to gather feedback. Yet its fundamental challenge is that the measures we used to evaluate performance are imperfect: we can't infer how hard or smart an employee is working based solely on outcomes. In this module, you’ll learn the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome, and see them at work in current companies, including an extended example from the NFL. By the end of this module, you’ll understand how to separate skill from luck and learn to read noisy performance measures, so that you can go into your next performance evaluation sensitive to the role of chance, knowing your environment, and aware of the four most common biases, so that you can make more informed data-driven decisions about your company's most valuable asset: its employees.
11 vídeos, 2 leituras
- Vídeo: Introduction to People Analytics
- Vídeo: Goals for the Course
- Vídeo: Course Outline and Overview
- Vídeo: People Analytics in Practice
- Vídeo: Performance Evaluation: the Challenge of Noisy Data
- Vídeo: Chance vs. Skill: the NFL Draft
- Vídeo: Finding Persistence: Regression to the Mean
- Vídeo: Extrapolating from Small Samples
- Vídeo: The Wisdom of Crowds: Signal Independence
- Vídeo: Process vs. Outcome
- Vídeo: Summary of Performance Evaluation
- Leyendo: Performance Analytics Slides PDF
- Leyendo: People Analytics in Action: Additional Reading
Nota atribuída: Performance Evaluation Quiz
SEMANA 2
Staffing
In this module, you'll learn how to use data to better analyze the key components of the staffing cycle: hiring, internal mobility and career development, and attrition. You'll explore different analytic approaches to predicting performance for hiring and for optimizing internal mobility, to understanding and reducing turnover, and to predicting attrition. You'll also learn the critical skill of understanding causality so that you can avoid using data incorrectly. By the end of this module, you'll be able to use data to improve the quality of the decisions you make in getting the right people into the right jobs and helping them stay there, to benefit not only your organization but also employee's individual careers.
12 vídeos, 2 leituras
- Vídeo: Introduction to Professor Bidwell
- Vídeo: Staffing Analytics Overview
- Vídeo: Hiring 1: Predicting Performance
- Vídeo: Hiring 2: Fine-tuning Predictors
- Vídeo: Hiring 3: Using Data Analysis to Predict Performance
- Vídeo: Internal Mobility 1: Analyzing Promotibility
- Vídeo: Internal Mobility 2: Optimizing Movement within the Organization
- Vídeo: Causality 1
- Vídeo: Causality 2
- Vídeo: Attrition: Understanding and Reducing Turnover
- Vídeo: Turnover: Predicting Attrition
- Vídeo: Staffing Analytics Conclusion
- Leyendo: Staffing Analytics Slides PDF
- Leyendo: Staffing Analytics in Action: Additional Reading
Nota atribuída: Staffing Quiz
SEMANA 3
Collaboration
In this module, you'll learn the basic principles behind using people analytics to improve collaboration between employees inside an organization so they can work together more successfully. You'll explore how data is used to describe, map, and evaluate collaboration networks, as well as how to intervene in collaboration networks to improve collaboration using examples from real-world companies. By the end of this module, you'll know how to deploy the tools and techniques of organizational network analysis to understand and improve collaboration patterns inside your organization to make your organization, and the people working within in it, more productive, effective, and successful.
7 vídeos, 2 leituras
- Vídeo: Introduction to Professor Haas
- Vídeo: Basics of Collaboration
- Vídeo: Describing Collaboration Networks
- Vídeo: Mapping Collaboration Networks
- Vídeo: Evaluating Collaboration Networks
- Vídeo: Measuring Outcomes
- Vídeo: Intervening in Collaboration Networks
- Leyendo: Collaboration Slides PDF
- Leyendo: Collaboration Research in Action: Additional Readings
Nota atribuída: Collaboration Quiz
SEMANA 4
Talent Management and Future Directions
In this module, you explore talent analytics: how data may be used in talent assessment and development to maximize employee ability. You'll learn how to use data to move from performance evaluation to a more deeper analysis of employee evaluation so that you may be able to improve the both the effectiveness and the equitability of the promotion process at your firm. By the end of this module, you'll will understand the four major challenges of talent analytics: context, interdependence, self-fulfilling prophecies, and reverse causality, the challenges of working with algorithms, and some practical tips for incorporating data sensitively, fairly, and effectively into your own talent assessment and development processes to make your employees and your organization more successful. In the course conclusion, you'll also learn the current challenges and future directions of the field of people analytics, so that you may begin putting employee data to work in a ways that are smarter, practical and more powerful.
9 vídeos, 2 leituras
- Vídeo: Talent Analytics: The Importance of Context
- Vídeo: Interdependence
- Vídeo: Self-fulfilling Prophecies
- Vídeo: Reverse Causality
- Vídeo: Special Topics: Tests and Algorithms
- Vídeo: Prescriptions: Navigating the Challenges of Talent Analytics
- Vídeo: Course Conclusion: Organizational Challenges 1
- Vídeo: Course Conclusion: Organizational Challenges 2 and Future Directions
- Vídeo: Goodbye and Good Luck!
- Leyendo: Talent Analytics and Conclusion Slides PDF
- Leyendo: Talent Management in Action: Additional Readings
Nota atribuída: Talent Management Quiz
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Desenvolvedores
Universidade da Pensilvânia
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Custo
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