Advanced Workforce Analytics with Python: 1 Day Workshop in Darwin
Description
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Course Overview:
This hands-on course is designed for professionals working in Workforce Planning, Reporting, and Analytics within contact centres (Voice, Digital, Webchat).
Participants will learn how to use Python within a Databricks environment to analyze agent and queue-level data, understand key operational metrics, and build a basic intraday forecast model.
The training combines Python fundamentals with real-world contact centre datasets and scenarios, enabling participants to move from raw data to actionable workforce insights.
Key Takeaways:
By the end of this training, you will be able to:
● Use Python in Databricks for workforce data analysis
● Understand and calculate key contact centre metrics
● Analyze intraday demand patterns across channels
● Prepare real-world operational data for forecasting
● Build a basic intraday forecast model
● Translate forecasts into actionable workforce insights
For general enquiries or booking your slot -
● +1 469 666 9332
Target Audience:
● Workforce Management (WFM) professionals
● Reporting & Analytics teams
● Operations analysts in contact centres
Anyone working with agent, queue, or intraday performance data
Our other courses that any professional should consider
● Mastering Business Writing Skills
● Core Management Skills
● Coaching Skills for Workplace Success
● Mastering the Art of Public Speaking
● Mastering Emotional Intelligence
● Stress Management and Resilience Training
To customize this course and host it in your organization
● +1 469 666 9332
Agenda
Module 1: Python Setup for Workforce Analytics (Databricks Focus)
● Introduction to Python in analytics workflows ● Overview of Databricks environment and notebooks ● Loading and exploring contact centre datasets ● Understanding data structure (interval-level, agent-level, queue-level) ● Activity
Module 2: Understanding Contact Centre Metrics
● Key metrics: ● AHT (Average Handling Time) ● SLA (Service Level) ● ASA (Average Speed of Answer) ● Occupancy & Shrinkage ● Differences across channels (Voice vs Chat vs Digital) ● How these metrics impact forecasting and staffing ● Activity
Module 3: Data Preparation for Workforce Analysis
● Cleaning agent and queue data ● Handling missing intervals and anomalies ● Aggregating data into intraday time buckets ● Structuring data for forecasting ● Activity: Prepare interval-level dataset for analysis
Module 4: Exploratory Analysis of Contact Centre Data
● Identifying intraday patterns (peaks, troughs) ● Channel-wise demand trends ● Visualizing volume, AHT, and SLA patterns ● Activity: Create intraday demand visualizations
Module 5: Introduction to Forecasting Concepts
● What is forecasting in workforce planning ● Intraday vs long-term forecasting ● Key drivers: volume, AHT, seasonality ● Simple forecasting approaches: ● Moving averages ● Trend-based estimation
Module 6: Building a Basic Intraday Forecast (Core Module)
● Creating interval-level forecasts using Python ● Applying simple models to historical data ● Adjusting for trends and variability ● Validating forecast accuracy ● Activity
Module 7: Translating Forecast into Workforce Insights
● Understanding staffing implications ● Comparing forecast vs actuals ● Identifying gaps in coverage ● Activity
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