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HR analytics, people analytics, workforce analytics -- whatever you call it, businesses are increasingly counting on their human resources departments to answer questions, provide insights, and make recommendations using data about their employees. In this course, you'll learn how to manipulate, visualize, and perform statistical tests on HR data through a series of HR analytics case studies.
Identifying the best recruiting sourceFree
In this chapter, you will get an introduction to how data science is used in a human resources context. Then you will dive into a case study where you'll analyze and visualize recruiting data to determine which source of new candidates ultimately produces the best new hires. The dataset you'll use in this and the other chapters in this course is synthetic, to maintain the privacy of actual employees.Welcome to the course!50 xpApplications of human resources (HR) analytics50 xpLooking at the recruiting data100 xpRecruiting and quality of hire50 xpIdentifying groups in data100 xpSales numbers by recruiting source100 xpAttrition rates by recruiting source100 xpVisualizing the recruiting data50 xpVisualizing the sales performance differences100 xpVisualizing the attrition differences100 xpDrawing conclusions50 xp
What is driving low employee engagement?
Gallup defines engaged employees as those who are involved in, enthusiastic about and committed to their work and workplace. There is disagreement about the strength of the connection between employee engagement and business outcomes, but the idea is that employees that are more engaged will be more productive and stay with the organization longer. In this chapter, you'll look into potential reasons that one department's engagement scores are lower than the rest.Analyzing employee engagement50 xpImporting the survey data100 xpWhich department has the lowest engagement?100 xpComparing other factors by department100 xpVisualizing the engagement data50 xpVisualizing several variables at once100 xpVisualizing several variables at once with facets100 xpDrawing conclusions from graphs50 xpIs that difference meaningful?50 xpStatistical significance - disengaged employees100 xpStatistical significance - vacation days100 xpDrawing conclusions from the tests50 xp
Are new hires getting paid too much?
When employers make a new hire, they must determine what the new employee will be paid. If the employer is not careful, the new hires can come in with a higher salary than the employees that currently work at the same job, which can cause employee turnover and dissatisfaction. In this chapter, you will check whether new hires are really getting paid more than current employees, and how to double-check your initial observations.Paying new hires fairly50 xpImporting the pay data100 xpIs the difference significant?100 xpOmitted variable bias50 xpOmitted variable bias examples50 xpWhat other differences exist?100 xpWhat does the pay difference look like now?100 xpAre hourly hires paid more?100 xpLinear regression50 xpNew hire pay: a simple regression100 xpNew hire pay: accounting for job levels100 xpDrawing conclusions from the tests50 xp
Are performance ratings being given consistently?
Performance management helps an organization keep track of which employees are providing extra value, or below-average value, and compensating them accordingly. Whether performance is a rating or the result of a questionnaire, whether employees are rated each year or more often than that, the process is somewhat subjective. An organization should check that ratings are being given with regard to performance, and not individual managers' preferences, or even biases (conscious or subconscious).Joining HR data50 xpImporting the two datasets100 xpJoining performance data100 xpPerformance ratings and fairness50 xpFocus on high performers100 xpComparing distributions of high performers100 xpChecking for omitted variable bias100 xpLogistic regression50 xpLinear and logistic regression50 xpPerformance ratings: a simple logistic regression100 xpPerformance ratings: accounting for job levels100 xpConclusions and recommendations50 xp
Improving employee safety with data
In many industries, workplace safety is a critical consideration. Maintaining a safe workplace provides employees with confidence and reduces costs for workers' compensation and legal liabilities. In this chapter, you'll look for explanations for an increase in workplace accidents.Employee safety50 xpImporting and joining the accident data100 xpWhere is the highest accident rate?100 xpWhere did the accident rate increase most?100 xpFocusing on the location of interest50 xpFocusing on the problem location100 xpBringing in more data100 xpChecking for omitted variables100 xpIs that change isolated to the problem location?100 xpExplaining the increase in accidents50 xpUsing regression to identify change drivers100 xpWhat can you conclude?50 xpConclusion50 xp
DatasetsRecruitment dataSurvey dataFair pay dataPerformance dataHR dataAccident dataHR data (2)Survey data (2)
PrerequisitesIntroduction to Regression in R
People Analytics Partner at Facebook
Ben is an HR Analytics Consultant at Capital Group. His passion is using data science to improve the way people experience work, manage others at work, and get work done. Ben holds degrees in mathematics, economics, & industrial and labor relations.
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