14 Topics
5 Days
70 Sessions
Programme Overview
Training Description
Who Should Attend
- Risk Managers
- Operations Managers
- Data and Business Intelligence Professionals supporting projects
- Product Managers and Product Owners
- Agile Project Managers and Scrum Masters
- Strategy and Planning Professionals
- Monitoring and Evaluation Professionals
- Consultants
- Digital Transformation Professionals
- Professionals seeking to integrate data analytics into project management
Session Objectives
- Understand the data science lifecycle and its stages
- Learn to communicate effectively with data scientists and analysts
- Identify key data sources and metrics for project success
- Master data governance and quality control principles
- Use data analytics to forecast project timelines and budgets
- Interpret common data visualizations and statistical models
- Manage risks and uncertainties using data-driven insights
- Understand the ethical considerations of using data in projects
- Learn to build a data-centric project plan
- Measure the business value and ROI of data initiatives
About the Course
The Data Science for Project Managers Training Course equips project professionals with the knowledge and practical skills to use data science, analytics, and emerging technologies to improve project planning, execution, monitoring, and decision-making. The course introduces participants to data-driven project management, predictive analytics, data visualization, forecasting, risk analytics, and the practical application of artificial intelligence in project environments.
Participants will learn how to collect, structure, interpret, and communicate project data to identify trends, predict potential challenges, optimize resources, monitor performance, and support evidence-based decisions. The training is designed for project professionals who want to leverage data science without necessarily becoming data scientists.
Curriculum & Topics
14 Topics | 70 Sessions
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Workshop 1.1:
The definition of data science and its key components
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Workshop 1.2:
The role of data in modern organizations
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Workshop 1.3:
The data science lifecycle from a PM's perspective
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Workshop 1.4:
The different types of data and their uses
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Workshop 1.5:
Key concepts and terminology in data science
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Workshop 2.1:
Identifying and sourcing relevant data
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Workshop 2.2:
Understanding data structures and formats
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Workshop 2.3:
The importance of data cleaning and preprocessing
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Workshop 2.4:
Common challenges in data preparation
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Workshop 2.5:
Tools and technologies for data handling
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Workshop 3.1:
The basics of descriptive statistics (mean, median, mode)
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Workshop 3.2:
Understanding probability and its role in risk assessment
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Workshop 3.3:
The concept of statistical significance
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Workshop 3.4:
Using data to test hypotheses
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Workshop 3.5:
The difference between correlation and causation
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Workshop 4.1:
The power of visual data
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Workshop 4.2:
Common chart types and their uses
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Workshop 4.3:
Creating effective and clear dashboards
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Workshop 4.4:
Telling a story with data to influence stakeholders
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Workshop 4.5:
The principles of good data visualization design
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Workshop 5.1:
The fundamental types of machine learning (supervised vs. unsupervised)
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Workshop 5.2:
Understanding common algorithms (e.g., regression, classification)
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Workshop 5.3:
The difference between training and testing data
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Workshop 5.4:
The concept of model accuracy and performance
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Workshop 5.5:
The role of a PM in an ML project
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Workshop 6.1:
Using data to define project scope and requirements
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Workshop 6.2:
Forecasting project timelines with historical data
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Workshop 6.3:
Budgeting and resource allocation using data insights
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Workshop 6.4:
The importance of a data strategy in the project plan
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Workshop 6.5:
Creating a data-centric project roadmap
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Workshop 7.1:
Identifying potential project risks from data
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Workshop 7.2:
Using predictive models to forecast issues
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Workshop 7.3:
Monitoring project health in real-time
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Workshop 7.4:
The role of data in proactive risk mitigation
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Workshop 7.5:
Case studies in data-driven risk management
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Workshop 8.1:
Defining key performance indicators (KPIs) with data
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Workshop 8.2:
Measuring project progress and team performance
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Workshop 8.3:
The importance of a data-driven feedback loop
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Workshop 8.4:
Using dashboards to monitor KPIs
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Workshop 8.5:
The difference between leading and lagging indicators
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Workshop 9.1:
The intersection of Agile methodologies and data science
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Workshop 9.2:
The iterative nature of data projects
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Workshop 9.3:
Using data to inform sprint planning and retrospectives
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Workshop 9.4:
The role of the PM in a data-focused Agile team
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Workshop 9.5:
Continuous delivery in a data-driven environment
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Workshop 10.1:
An overview of common data science tools (e.g., Python, R)
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Workshop 10.2:
Introduction to data visualization tools (e.g., Tableau, Power BI)
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Workshop 10.3:
The use of cloud platforms for data projects (e.g., AWS, GCP)
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Workshop 10.4:
Collaborative tools for data teams
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Workshop 10.5:
Understanding the technology stack of a data project
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Workshop 11.1:
Using data to analyze business processes
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Workshop 11.2:
The importance of business value metrics
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Workshop 11.3:
A/B testing and experimentation
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Workshop 11.4:
The role of the PM in creating a data-driven culture
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Workshop 11.5:
Translating data insights into business actions
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Workshop 12.1:
Communicating data insights to non-technical stakeholders
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Workshop 12.2:
Fostering a collaborative environment between PMs and data teams
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Workshop 12.3:
Overcoming communication barriers
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Workshop 12.4:
The importance of a shared vocabulary
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Workshop 12.5:
Techniques for effective data-focused meetings
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Workshop 13.1:
The ethical responsibilities of a data-focused PM
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Workshop 13.2:
Understanding bias in data and its impact
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Workshop 13.3:
Data privacy and security regulations
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Workshop 13.4:
The importance of transparency and explainability in AI
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Workshop 13.5:
Building a framework for responsible data use
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Workshop 14.1:
Analyzing real-world data science projects
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Workshop 14.2:
Hands-on exercises with sample datasets
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Workshop 14.3:
Building a simple project plan for a data initiative
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Workshop 14.4:
Presenting data-driven insights to peers
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Workshop 14.5:
Applying course concepts to a simulated project
