Learn the fundamentals of human resources management, including the processes involved in people analytics.
People analytics is an approach that examines all people procedures, capabilities, obstacles, and opportunities in the workplace to improve these systems and achieve long-term business success. It is deeply data-driven and goal-focused. People Analytics 101: HR Analytics Fundamentals certification course is designed by Unlock HR- People Analytics, HR Analytics & Talent Analytics, which is presented by Udemy for individuals who want to learn how to make data-driven decisions about their teams, employees, and standard operating procedures.
People Analytics 101: HR Analytics Fundamentals online course encompasses 9 hours of comprehensive lectures supported by 12 downloadable resources and 6 articles which aim to provide individuals with the knowledge and skills necessary to use statistical data and technology on unutilized but crucial human data, enabling them to make business decisions and manage their organization. People Analytics 101: HR Analytics Fundamentals online classes explain the topics associated with the employee life cycle, HR metrics, data collection, data preparation, model evaluation, and more.
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Udemy
After completing the People Analytics 101: HR Analytics Fundamentals online certification, individuals will gain a better understanding of the basics of human resources as well as will obtain knowledge of the concepts involved with people analytics and HR analytics. In this people analytics course, individuals will explore the concepts involved with the statistical model building as well as will acquire the skills to convert business problems into statistical problems Individuals will learn about the methodologies to measure variability, central tendency, and shape of data as well as will acquire the knowledge of the concepts involved with data collection, data preparation. In this people analytics certification, individuals will also learn about strategies involved with hypothesis testing, univariate analysis, bi-variate analysis, and model evaluation as well as will acquire an understanding of the fundamentals associated with machine learning including supervised and unsupervised learning.
Knowledge Check (Model Evaluation)
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