HR Analytics - Workforce Management using R

Explore workforce management through data-driven insights that optimize recruiting, budgeting, forecasting, scheduling, and analytics to boost productivity, reduce costs, and lower turnover.

Understand the anatomy of a statistical model through a step-by-step approach to solving a business problem. Follow data discovery and collection, data preparation, model selection, and rigorous evaluation and validation.

Download and install R and RStudio on Windows or Mac, create and run scripts, import data with read csv or text files, and install packages for HR analytics.

Explore the call center data set column by column, including customer id, agent id, priority, call times, outcome, satisfaction, shift, demographics, and fdr, to guide r-based analytics and staffing decisions.

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.

Learn to perform univariate analysis in R, describing numerical and categorical variables, identify missing values and outliers, and use box plots and summary statistics to understand data for HR analytics.

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.

Analyze univariate variables from HR analytics data in R, including handling time, company tenure, and waiting time, using box plots, histograms, and pie charts to reveal distributions and outliers.

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.

Explore Erlang C theory for workforce management in call centers, using traffic intensity and average handling time to estimate staffing, service levels, and wait probabilities.

Discover data preparation in R for Erlang, including separating date and time, converting dates, dropping unused columns, and creating hourly call counts for workload analysis.

Learn to estimate call center staffing using Erlang concepts in R, calculating traffic intensity, service level, and required agents to meet an 80 percent, 20-second target.

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.