Course Details

Data Analytics for Project Performance Optimization Training Course: Using Big Data and analytics to gain insights and make data-driven project decisions.

Introduction

Unleash the power of data to transform your project outcomes with our "Data Analytics for Project Performance Optimization" training course. In today's data-rich environment, relying on intuition alone is no longer enough for project success. This intensive 10-day program equips project professionals with the essential skills to leverage Big Data and advanced analytics, gaining profound insights into project health, performance, and potential risks. Learn to track, analyze, and visualize project data to make smarter, proactive decisions, optimize resource allocation, enhance predictability, and consistently drive superior project delivery. Become a data-driven project leader and elevate your strategic impact.

Duration

10 Days

Target Audience

This course is designed for project managers, program managers, PMO professionals, business analysts, data analysts, and team leads who want to enhance their ability to leverage data for improved project performance and decision-making. It is particularly beneficial for those in:

• Organizations with access to significant project data (historical or real-time).
• Roles requiring advanced forecasting, risk prediction, and resource optimization.
• Environments striving for greater project predictability and efficiency.
• PMOs looking to standardize data collection and reporting for better portfolio visibility.
• Professionals seeking to transition towards a more data-driven project management approach.

Course Objectives

Upon successful completion of the "Data Analytics for Project Performance Optimization" training course, participants will be able to:

• Understand the fundamental concepts of Big Data, data analytics, and their application in project management.
• Identify key project performance indicators (KPIs) and relevant data sources for effective analysis.
• Apply various data collection, cleansing, and preparation techniques for robust project data analysis.
• Utilize descriptive, diagnostic, predictive, and prescriptive analytics to gain actionable insights from project data.
• Implement statistical methods and modeling for enhanced forecasting, risk assessment, and resource optimization.
• Create compelling data visualizations and interactive dashboards for effective project reporting and stakeholder communication.
• Leverage popular data analysis tools (e.g., Excel, Power BI, Tableau, Python basics) for practical application.
• Understand the ethical considerations, data privacy, and data governance requirements in project analytics.
• Develop strategies for fostering a data-driven culture within project teams and across the organization.
• Formulate a strategic roadmap for implementing data analytics capabilities within their project management practices.

Course Modules

Module 1: Introduction to Data Analytics in Project Management

• What is Data Analytics? Overview of Big Data concepts and sources in projects.
• The "Why" of data-driven project management: Enhanced predictability, efficiency, and decision-making.
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive.
• The Project Data Lifecycle: Collection, Storage, Processing, Analysis, Visualization, Action.
• Case studies of data analytics driving project success.

Module 2: Project Data Sources and Collection Strategies

• Identifying internal project data sources: Schedules, budgets, resource logs, issue trackers, quality reports.
• Leveraging external data sources: Market trends, industry benchmarks, supplier performance.
• Data collection methods: Automated systems, manual entry, surveys, APIs.
• Ensuring data quality: Accuracy, completeness, consistency, timeliness.
• Data governance principles for project data.

Module 3: Data Cleansing, Preparation, and Transformation

• The importance of clean data for reliable analysis.
• Techniques for handling missing values, outliers, and duplicates.
• Data transformation: Normalization, aggregation, feature engineering for PM context.
• Using Excel for basic data cleaning and manipulation.
• Introduction to data preparation tools/concepts (e.g., Power Query, basic Python data frames).

Module 4: Descriptive Analytics for Project Performance

• Core descriptive statistics: Mean, median, mode, standard deviation, variance.
• Frequency distributions, histograms, and box plots for understanding data shape.
• Analyzing project baselines vs. actuals: Schedule variance, cost variance.
• Key Performance Indicators (KPIs) for project health: Burn-down/up rates, cycle time, lead time.
• Identifying patterns and trends in historical project data.

Module 5: Diagnostic Analytics: Understanding Why

• Root Cause Analysis with data: Using data to confirm underlying issues.
• Correlation vs. Causation in project data analysis.
• Drill-down analysis: Investigating specific anomalies or deviations.
• A/B testing concepts for project process improvements.
• Case studies of diagnostic analysis revealing project problems.

Module 6: Predictive Analytics for Project Forecasting and Risk

• Introduction to forecasting techniques: Time series analysis, regression analysis (simple linear regression).
• Predicting project completion dates and costs with higher accuracy.
• Risk Prediction: Using historical data to identify potential project risks (e.g., schedule delays, budget overruns).
• Probabilistic forecasting and Monte Carlo simulations (conceptual understanding).
• Leveraging ML models (without coding) for predictive insights in PM tools.

Module 7: Prescriptive Analytics: Recommending Actions

• Understanding optimization algorithms and their application in PM.
• Recommending optimal resource allocation, task sequencing, and risk mitigation strategies.
• Decision trees and rule-based systems for guiding project choices.
• Simulating the impact of different decisions on project outcomes.
• Case studies of prescriptive analytics driving better project outcomes.

Module 8: Data Visualization and Storytelling

• Principles of effective data visualization for project managers.
• Choosing the right chart type for different project data (bar, line, scatter, pie, Gantt).
• Designing interactive dashboards using tools like Power BI or Tableau.
• Storytelling with data: Presenting insights clearly and persuasively to stakeholders.
• Tailoring visualizations for different audiences (executives, teams).

Module 9: Tools and Technologies for Project Analytics

• Microsoft Excel for Project Analytics: Advanced formulas, pivot tables, charts.
• Introduction to Power BI/Tableau for Project Dashboards: Connecting data sources, building reports.
• Overview of specialized Project Analytics software.
• Conceptual introduction to programming languages for data (Python/R) and their libraries for PM.
• Data security and privacy considerations for analytics platforms.

Module 10: Building a Data-Driven PM Culture and Strategy

• Fostering data literacy and analytical thinking within project teams.
• Overcoming resistance to data-driven decision-making.
• Establishing a PMO-led data analytics strategy.
• Measuring the ROI of data analytics initiatives in project management.
• Personal action plan for integrating data analytics into your daily project work.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com