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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hr-analytics-course/
课程评论：没有评论
课程名称：人力资源分析硕士课程（使用 Excel、Python 和 R） 课程概述： 人力资源分析也称为人才分析，是一种帮助人力资源经理和高层管理者基于数据做出决策的分析方法。该课程将指导学员使用统计学和技术对未被充分利用但极其重要的人力数据进行分析，从而帮助企业做出更好的业务决策和管理。课程内容从基础的均值计算入手，逐步深入到复杂的主题，如文本分析，最后进入机器学习技术。完成课程后，学员能够帮助公司更好地推动投资回报率。传统的方法在长期内已不足以达到所需的效果，因此我们设计了这一解决方案，使学员能够以简单、直观、自主的学习方式，独立掌握解决问题的技巧。 课程特色： - 从零开始学习应用统计学。 - 同时学习 R 和 Python 的分析技巧。 - 识别数据集中的因变量和自变量。 - 理解数据准备的步骤。 - 掌握测量集中趋势、变异性及数据形态的各种方法。 - 理解假设检验、单变量及双变量分析的步骤。 - 学习特征工程的概念。 - 理解统计模型构建的概念。 - 确定商业问题及其重要性。 - 理解机器学习的概念，包括监督学习和无监督学习技术。 - 完成4个实践案例研究。 - 了解20多种图表/绘图方法。 - 最重要的是，在人力资源数据上应用机器学习，预测未来的洞察。 本课程结构简单明了，即使是没有或仅具备基础分析知识的学员，也能够顺利完成课程内容。
HR analytics is also known as people analytics or you can say talent analytics. It is kind of analytics which helps HR managers, executives to make data-driven decisions about their employee or the workforce. It gives you expertise in using statistics, technology on unused but very important people's data which can help you in making better business decisions and management for your company. In this course, we take you on a journey where you start from a simple topic of calculating mean and move on to many complex topics such as text analytics. Hence you kickstart from statistics and land on machine learning techniques.Once you have completed the course, you can help your company to better drive the ROI. Classic approaches are not sufficient in getting the required result in the long run.To overcome this gap we came up with a solution where you can learn the techniques of solving these problems on your own in a very simple and intuitive self-paced learning method.We have tried to create a very simple structure for this course so even if you have no knowledge or very basic knowledge of analytics then even you won't face any problem throughout the course. In this course you will:Learn applied statistics right from scratch.Simultaneously learn analytics on R and Python.Identify the dependent and independent variables in your dataset.Understand the steps involved in data preparation.Various methods to measure Central Tendency, Variability, and Shape of data.Understand the steps involved in Hypothesis Testing, Univariate, and Bi-variate Analysis.Learn the concepts of Feature Engineering.Understand the concepts of Statistical model building.Identify a business problem and its importance. Understand the concept of Machine Learning - Supervised and Unsupervised Learning Techniques.4 Hands-on case studies.More than 20 types of charts/plots.And the most important is applying machine learning on HR Data and predicting futuristic insights.
