Python Tutorial: Human Resources Analytics: Predicting Employee Churn in Python | Intro
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Hello and welcome to "HR analytics in Python" course. My name is Hrant Davtyan, I am a Business Analyst teaching Data Science and providing consultancy related to statistics.
Among all of the business domains, HR is still the least disrupted. However, the latest developments in Data collection and analysis tools and technologies allow for data driven decision-making in all dimensions including HR. As a consequence, HR analytics is a growing field and I believe it is the correct time to tap into that industry.
HR analytics is also known as People analytics and it is nothing else than a data-driven approach to managing people at work.
There are many problems in HR that can be addressed using data-driven approach. Among those are decisions related to employee hiring and retention, performance evaluation, collaboration and else. In this course, we will concetrate on Predicting employee turnover which is related to the first 2 bullet points: Hiring and retention.
Employee turnover is the process of employees leaving the company also known as employee attrition or employee churn. When skilled employees leave, this can be very costly for the company, thus firms are interested in predicting turnover beforehand. Having that information in hand, companies can change their strategy to retain good workers or start the hiring process of new employees on time.
In this course, we will use a sample employee dataset with variables that describe employees in the company to predict their turnover and understand what are the most important features affecting it. The 1st chapter will concetrate on descriptive analytics, where we will transform the dataset and make it ready for developing the predictive model. In the 2nd chapter we
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