
Explore workforce management through data-driven insights that optimize recruiting, budgeting, forecasting, scheduling, and analytics to boost productivity, reduce costs, and lower turnover.
Verify data by loading an xlsx file with the lead excel package, read_xlsx, and inspect dim and head or tail to confirm data integrity before cleaning.
Explore feature engineering with data transformation, variable creation, and dimension reduction, using information value, PCR, and factor analysis. Learn to encode categories, compute time-based features, and drop noninformative columns.
Learn bivariate analysis in R by pairing two variables—numerical with numerical, or numerical/categorical—with visuals and aggregation by agent ID to examine customer satisfaction and handling time.
Use the automated workforce management solution to plug in an Excel input and run an R function that outputs the required agents to meet the 80% service level, considering shrinkage.
This course revolves around finding the right people to be deployed for a given task at the right time, to ensure that the customer expectations and metrics are being met. As an HR professional or a business partner, you play an important role, which is managing the workforce.
In this course:
You will learn the basics of workforce management.
Tackle the problem of shortage of employees by performing resource management.
We will solve a business problem that involves customers getting dissatisfied with the customer care service, due to non-availability and high waiting time which leads to customers abandoning the calls.
You will study the call volume data and relate with employee demographics.
Learn about Erlang C and it's applications in Rstudio to predict the number of employees required on an hourly interval to meet customer expectations.
Master practical skills to solve an HR business problem using a Step-by-step approach called “Anatomy of a Statistical Model”.
Understand how to prepare and explore the data for meaningful insights.
Applying feature engineering techniques to get in-depth knowledge hidden inside the data.