A successful data science function in the HR department requires balancing the analytics maturity of the business and HR leaders with the data scientist’s skills. It is essential and fascinating to explore how data science and HR needs are knitted.
I asked ChatGPT some questions about my current struggle in publishing R Bookdown and could not resist expanding the discussion to a broader context. Beyond information, I asked for advice, opinion, feedback, compliment, and prediction. I also brought my chutzpa to the conversation.
My People Analytics and HR-Tech reading list on Kindle includes +60 items! Find here inspiration, practical guidance, validation for practices, new ideas and innovative tools, an “open door” to a professional community.
Simulating work with a data scientist in HR and People Analytics use case: The gender pay gap. Analyzing continuous variables instead of categorical variables, swapping between ANOVA and Linear Regression, and additional insights based on actual data.
What should HR professionals expect when working with a data scientist? What should HR professionals do for a successful data science project? How should you deal with insignificant results in the analysis? The article addresses these questions.
The Employee lifetime value is a scheme that connects the people processes to the business outcomes. It refers to the expected value the organization gains in the entire time an employee in a particular role spends working, quantifying how “people are our most important asset.”
The culture of the People Analytics community is remarkably open. While datasets, analytics, and insights are restricted, experiences, resources, and advice are generously shared. It inspired me to list the ABCs of success: autodidact habits, business understanding, and coding skills.
Comprehensive list of Littal’s professional training programs, public speaking engagements, media coverage and recognition, 2017-2021
Why are we failing to see the hidden patterns in the gender pay gap? How can HR professionals work better with data scientists to spot hidden patterns? How can we generalize this case study for up-skilling and re-skilling in critical thinking and an analytical mindset?
To demonstrate how visualization is helpful, I picked a relatively common topic in People Analytics: Employee absenteeism. I present two types of hypotheses regarding absenteeism, specifically, how employee background and work characteristics are related to absenteeism.