Operational Data Science Training Course

Name: Operational Data Science and Process Improvement Training Course
Availability: InStock

Use operational data to measure performance, analyze variation and bottlenecks, forecast outcomes, detect anomalies, and verify improvement benefits.

Operational Data Science Training Course

At a glance

Duration: 1 Week
Format: Classroom
Cities: Milan, Johannesburg, London, Barcelona, Montreux, Accra and more
Next session: 19 – 23 October 2026, Milan
Average fee: 5,800 €

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Operational Data Science and Process Improvement Training Course

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Overview

Operational Data Science and Process Improvement Training Course is a one-week intermediate course for operations analysts, improvement teams, performance managers, data practitioners, and operational leaders who leave with an Operational Analytics Improvement Pack. Participants define process measures, prepare operational data, analyze variation and bottlenecks, forecast demand and performance, detect anomalies, investigate contributing factors, evaluate interventions, and govern improvement evidence. The course separates observed associations from tested causes. Agile Leaders Training Center develops operational data science capability.

Who Should Attend

Operations functions responsible for capacity, flow, service, reliability, and daily performance
Process improvement functions responsible for baselines, opportunities, interventions, and benefits
Performance functions responsible for KPIs, reporting, targets, trends, and review routines
Data and analytics functions responsible for operational datasets, models, forecasts, and insights
Quality functions responsible for variation, capability, defects, and corrective action
Maintenance and supply functions responsible for asset, inventory, demand, and exception decisions

The course assumes participants can work with operational tables, KPIs, and basic descriptive statistics, and leaves out advanced machine-learning engineering, software development, and formal quality certification preparation.

Departments and Industries

The course supports departments and industries that use process data to improve performance, reliability, quality, and resource decisions.

Operations, quality, performance, maintenance, supply chain, and continuous improvement
Data analytics, business intelligence, planning, finance, and customer operations
Manufacturing, logistics, utilities, transport, facilities, and field services
Healthcare, hospitality, retail, telecommunications, and financial services
Public services, education, professional services, and technology operations

Learning Objectives

By the end of this course, participants will be able to:

Build reliable operational measures and analytical datasets
Analyze process variation, flow, and bottlenecks
Apply forecasting and anomaly-detection methods
Evaluate contributing factors and improvement hypotheses
Compare interventions and track operational benefits
Build an Operational Analytics Improvement Pack

Course Agenda

Day 1: Frame Operational Questions and Measures

Operational Decision and Problem Statement
Process Boundary and Data Flow Map
KPI Definition and Measurement Register
Data Quality and Sampling Checklist
Operational Analytics Baseline

Day 2: Analyze Variation, Flow, and Capability

Trend and Distribution Profile
Common-Cause and Special-Cause Review
Process Capability Assessment
Cycle-Time and Bottleneck Analysis
Segment and Driver Comparison

Day 3: Forecast Performance and Detect Anomalies

Time-Series Pattern and Seasonality Review
Operational Forecast and Uncertainty Range
Anomaly Detection Sensitivity Settings
Exception Prioritization Matrix
Forecast and Alert Validation Record

Day 4: Evaluate Causes and Improvement Options

Contributing-Factor Analysis
Root-Cause Hypothesis Register
Experiment and Intervention Design
Scenario and Optimization Comparison
Risk, Constraint, and Trade-Off Review

Day 5: Govern Improvements and Benefits

Operational Analytics Dashboard
Improvement Action and Ownership Plan
Benefit Baseline and Tracking Register
Exercise: Investigate an Operational Exception
Capstone: Operational Analytics Improvement Pack

Practical Exercises

The course uses suggested activities based on service, production, maintenance, inventory, capacity, and customer-operation scenarios.

Suggested activity: define an operational decision, create measures, assess data quality, and establish a baseline.
Suggested activity: distinguish variation patterns, examine capability, and identify flow constraints and bottlenecks.
Suggested activity: build a forecast, flag anomalies, tune alert sensitivity, and validate exceptions.
Suggested activity: compare improvement options, record assumptions and risks, and design benefit tracking.

FAQs

Who suits operational data science training, and what does it assume?

Operations, improvement, performance, analytics, quality, maintenance, and supply functions suit the training; it assumes routine experience with operational tables, KPIs, and basic descriptive statistics.

How does operational data science differ from business reporting?

Business reporting describes performance through established measures. Operational data science extends this work through variation analysis, forecasting, anomaly detection, factor investigation, experiments, scenarios, and validation tied to specific process decisions.

Why distinguish common-cause and special-cause variation?

The distinction helps teams avoid reacting to routine process fluctuation as if every movement were exceptional, while directing investigation toward unusual signals and choosing system-level or event-specific responses appropriately.

How should operational anomalies be managed?

Anomalies should be detected against an expected pattern, prioritized by operational impact, validated for data quality, investigated for contributing factors, assigned for response, documented, and monitored for recurrence and control effectiveness.

How are improvement benefits verified?

Benefits are verified by defining a baseline, target, calculation rule, time window, owner, data source, comparison approach, confounding factors, costs, risks, and review cadence before claiming an improvement.

Conclusion

Participants take back an Operational Analytics Improvement Pack containing decision statements, process maps, KPI definitions, data checks, baselines, variation and capability reviews, forecasts, anomaly records, hypotheses, intervention designs, dashboards, and benefit trackers. It connects analytical evidence to controlled improvement decisions. The pack gives teams a repeatable structure for investigation, action, validation, and learning.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 74 events

Location	Dates	Duration	Mode	Price
Dubai	Week 05, 2027 1 – 5 February 2027	1 Week	Onsite	€4,500
Abu Dhabi	Week 05, 2027 1 – 5 February 2027	1 Week	Onsite	€4,700
Madrid	Week 06, 2027 8 – 12 February 2027	1 Week	Onsite	€5,700
Bangkok	Week 06, 2027 14 – 18 February 2027	1 Week	Onsite	€6,000
Cairo	Week 08, 2027 22 – 26 February 2027	1 Week	Onsite	€4,100
Berlin	Week 08, 2027 22 – 26 February 2027	1 Week	Onsite	€5,700
Nairobi	Week 08, 2027 28 February – 4 March 2027	1 Week	Onsite	€4,500
Manama	Week 09, 2027 7 – 11 March 2027	1 Week	Onsite	€4,700
Kuala Lumpur	Week 10, 2027 8 – 12 March 2027	1 Week	Onsite	€5,200
San Diego	Week 11, 2027 15 – 19 March 2027	1 Week	Onsite	€14,000
Rome	Week 12, 2027 22 – 26 March 2027	1 Week	Onsite	€5,700
Jakarta	Week 12, 2027 22 – 26 March 2027	1 Week	Onsite	€5,700
Toronto	Week 12, 2027 28 March – 1 April 2027	1 Week	Onsite	€12,000
Istanbul	Week 13, 2027 29 March – 2 April 2027	1 Week	Onsite	€4,500
London	Week 14, 2027 5 – 9 April 2027	1 Week	Onsite	€5,700
Vienna	Week 14, 2027 5 – 9 April 2027	1 Week	Onsite	€5,700
Marbella	Week 14, 2027 11 – 15 April 2027	1 Week	Onsite	€5,700
Tashkent	Week 16, 2027 25 – 29 April 2027	1 Week	Onsite	€4,500
Geneva	Week 17, 2027 2 – 6 May 2027	1 Week	Onsite	€6,200
Dubai	Week 19, 2027 10 – 14 May 2027	1 Week	Onsite	€4,500

Frequently asked questions

What does this course cover?

OverviewOperational Data Science and Process Improvement Training Course is a one-week intermediate course for operations analysts, improvement teams, performance managers, data practitioners, and operational leaders who leave with an Operational Analytics Improvement Pack. Participants define process measures, prepare operational data, analyze variation…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Operational Data Science Training Course