Data Analytics for Project Managers and Evidence-Based Decisions Training Course

Course overview

Project managers are expected to explain whether delivery is on track, where risk is building, and which corrective action is justified. Yet project data is commonly dispersed across schedules, cost reports, RAID logs, timesheets and stakeholder updates. This makes it easy to report activity without demonstrating performance. The result is late escalation, weak forecasts and decisions based on confidence rather than evidence. This course equips project managers to turn operational project data into clear analysis that supports governance decisions, resource trade-offs and credible stakeholder communication.

Participants learn to define decision-focused measures, structure project datasets and test the quality of source data before drawing conclusions. The course applies descriptive, diagnostic and predictive techniques to schedule, cost, resource, risk and benefits data. Participants work with Excel, Power BI and Microsoft Project data to calculate and interpret KPIs including schedule variance, cost variance, CPI, SPI, estimate at completion, trend indicators and risk exposure. They also learn to distinguish correlation from causation, identify misleading charts, formulate evidence-based recommendations and communicate findings to executive audiences.

Teaching combines instructor-led demonstrations with guided analysis of a realistic project portfolio case. Participants build formulas, pivot tables, variance analyses, dashboard visuals and forecast scenarios, then practise presenting an evidence-led project review. By the end of the week, each participant leaves with a reusable project performance dashboard specification, an analysis workbook and a 90-day application plan for improving reporting and decision routines in their own project environment.

The course is designed for practising project managers who already work with project plans, budgets or status reports and need stronger analytical judgement. It is equally valuable for programme, PMO and delivery professionals responsible for converting project information into decisions that leaders can act on.

Course objectives

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

• Define a decision-focused project measurement framework linking KPIs to schedule, cost, risk and benefits decisions
• Clean and structure project data sets using Excel tables, validation rules and consistent data definitions
• Calculate schedule and cost performance measures including SV, CV, SPI, CPI and estimate at completion
• Build pivot-table analyses to identify variance drivers by work package, supplier, resource and project phase
• Create a Power BI project dashboard with trend, variance, milestone and risk-exposure visualisations
• Apply earned value management methods to forecast likely cost and schedule outcomes
• Evaluate data quality, outliers, correlation and assumptions before making project recommendations
• Produce an evidence-based project review pack containing findings, decision options and recommended actions

Benefits of attending

Target competencies

Who should attend

• Project Managers — who must explain delivery performance and justify corrective action to sponsors
• Senior Project Managers — who manage complex baselines, forecasts and cross-functional delivery decisions
• Programme Managers — who need comparable evidence across multiple projects and workstreams
• PMO Managers — who design reporting standards and challenge the reliability of project status data
• Project Controls Professionals — who analyse schedule, cost and earned value information for governance forums
• Delivery Managers — who need to prioritise resources and risks using measurable operational evidence

Requirements and prerequisites

Participants should have practical experience working on projects and be familiar with core project-management concepts such as scope, milestones, work breakdown structures, schedules, budgets, risks and status reporting. They should be able to use basic Excel functions, filter and sort data, and interpret simple charts; experience with pivot tables is helpful but not essential. Access to a laptop with Microsoft Excel is expected for exercises. Prior Power BI, Microsoft Project, SQL, statistics or formal earned value management training is not required. The course teaches the analytical methods and tool workflows needed for project decision-making rather than advanced data science or programming.

Training methodology

The course uses short instructor-led briefings followed by hands-on analysis in Excel and Power BI. Participants work through a connected project portfolio case containing a schedule extract, cost data, resource records, risk log and stakeholder reporting pack. Exercises require them to clean data, calculate earned value measures, investigate exceptions and build visual outputs rather than merely review examples. Small-group review sessions simulate a project board, where participants challenge assumptions and defend recommendations. The final session converts the case learning into an individual dashboard and reporting implementation plan.

Course outline

Day 1: Framing project decisions with data

• Decision questions for project governance and stage-gate reviews
• Project data sources: schedules, cost systems, RAID logs and timesheets
• KPI design using leading, lagging and predictive indicators
• Operational definitions for scope, schedule, cost, quality and benefits measures
• Data quality checks for completeness, consistency, timeliness and accuracy
• Excel tables, structured references and data-validation controls
• Baseline, actual and forecast data relationships in project reporting

Workshop: Participants audit a flawed project status dataset and produce a data dictionary, quality issue log and decision-question map.

Day 2: Analysing schedule, cost and resource performance

• Work breakdown structures and control accounts as analysis dimensions
• Schedule variance and milestone trend analysis
• Cost variance analysis by work package and cost category
• Earned value measures: PV, EV, AC, SV, CV, SPI and CPI
• Estimate at completion and estimate to complete forecasting formulas
• Excel pivot tables, slicers and calculated fields for project analysis
• Resource utilisation, capacity variance and workload trend measures

Workshop: Participants build an Excel performance analysis workbook that identifies the principal cost and schedule variance drivers.

Day 3: Risk, uncertainty and evidence-based forecasting

• Risk exposure calculation using probability-impact scoring
• Risk-adjusted forecasting and contingency consumption analysis
• Trend analysis using run charts and cumulative-flow indicators
• Outlier detection and exception thresholds for project controls
• Correlation versus causation in project performance data
• Scenario analysis for staffing, scope and supplier-delay assumptions
• Confidence statements, assumptions logs and limitations in forecasts

Workshop: Participants model three delivery scenarios for a delayed project and produce a risk-adjusted forecast with stated assumptions.

Day 4: Building dashboards that support action

• Dashboard requirements for sponsors, project boards and delivery teams
• Power BI data import and transformation with Power Query
• Data-model relationships between project, work package, cost and risk tables
• DAX measures for CPI, SPI, forecast variance and risk exposure
• Visual selection for trends, exceptions, milestones and portfolio comparisons
• Drill-through, filters and tooltips for management investigation
• Chart integrity, accessibility and avoidance of misleading visualisations

Workshop: Participants create a Power BI project performance dashboard with KPI cards, variance trends, milestone status and a risk view.

Day 5: Presenting analysis and embedding better decisions

• Structuring an evidence-based project review narrative
• Translating analysis into decision options and corrective actions
• Escalation thresholds and governance triggers for performance exceptions
• Communicating forecast uncertainty to executive stakeholders
• Challenging data assumptions and handling stakeholder questions
• Project reporting cadence, ownership and control procedures
• Ninety-day implementation planning for analytics-enabled project controls

Workshop: Participants present a project board review pack, receive peer challenge, and complete a 90-day plan for applying their dashboard and analysis methods.

Tools & standards covered

Microsoft Excel, Microsoft Power BI, Microsoft Project, PMI Earned Value Management Practice Standard

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions