14 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Project Managers
- Program Managers
- Business Analysts
- Team Leads
- Scrum Masters
- Product Owners
- Functional Managers
- IT Managers
- Agile Practitioners
- Individuals seeking to manage data-centric projects
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
In an increasingly data-driven world, the ability to leverage data science is becoming a critical skill for modern project managers. This "Unlocking Insights: Data Science for Project Managers Training Course" is a specialized program designed to bridge the gap between project management and data analytics. This course will empower you to understand data science methodologies, communicate effectively with data teams, and use data-driven insights to make better project decisions, optimize resource allocation, and ultimately, drive successful project outcomes.
Over this intensive 10-day period, you will move from a foundational understanding of data concepts to a practical application of data science principles within your projects. You will learn about the data lifecycle, common machine learning algorithms, and how to interpret project-related data to predict risks, monitor progress, and measure performance. This program is not about turning you into a data scientist, but rather about equipping you with the knowledge to strategically manage data-intensive projects and confidently lead your teams towards data-informed success.
Curriculum & Topics
14 Topics | 10 Days
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Subtopic 1.1:
The definition of data science and its key components
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Subtopic 1.2:
The role of data in modern organizations
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Subtopic 1.3:
The data science lifecycle from a PM's perspective
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Subtopic 1.4:
The different types of data and their uses
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Subtopic 1.5:
Key concepts and terminology in data science
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Subtopic 2.1:
Identifying and sourcing relevant data
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Subtopic 2.2:
Understanding data structures and formats
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Subtopic 2.3:
The importance of data cleaning and preprocessing
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Subtopic 2.4:
Common challenges in data preparation
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Subtopic 2.5:
Tools and technologies for data handling
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Subtopic 3.1:
The basics of descriptive statistics (mean, median, mode)
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Subtopic 3.2:
Understanding probability and its role in risk assessment
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Subtopic 3.3:
The concept of statistical significance
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Subtopic 3.4:
Using data to test hypotheses
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Subtopic 3.5:
The difference between correlation and causation
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Subtopic 4.1:
The power of visual data
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Subtopic 4.2:
Common chart types and their uses
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Subtopic 4.3:
Creating effective and clear dashboards
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Subtopic 4.4:
Telling a story with data to influence stakeholders
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Subtopic 4.5:
The principles of good data visualization design
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Subtopic 5.1:
The fundamental types of machine learning (supervised vs. unsupervised)
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Subtopic 5.2:
Understanding common algorithms (e.g., regression, classification)
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Subtopic 5.3:
The difference between training and testing data
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Subtopic 5.4:
The concept of model accuracy and performance
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Subtopic 5.5:
The role of a PM in an ML project
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Subtopic 6.1:
Using data to define project scope and requirements
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Subtopic 6.2:
Forecasting project timelines with historical data
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Subtopic 6.3:
Budgeting and resource allocation using data insights
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Subtopic 6.4:
The importance of a data strategy in the project plan
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Subtopic 6.5:
Creating a data-centric project roadmap
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Subtopic 7.1:
Identifying potential project risks from data
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Subtopic 7.2:
Using predictive models to forecast issues
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Subtopic 7.3:
Monitoring project health in real-time
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Subtopic 7.4:
The role of data in proactive risk mitigation
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Subtopic 7.5:
Case studies in data-driven risk management
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Subtopic 8.1:
Defining key performance indicators (KPIs) with data
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Subtopic 8.2:
Measuring project progress and team performance
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Subtopic 8.3:
The importance of a data-driven feedback loop
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Subtopic 8.4:
Using dashboards to monitor KPIs
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Subtopic 8.5:
The difference between leading and lagging indicators
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Subtopic 9.1:
The intersection of Agile methodologies and data science
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Subtopic 9.2:
The iterative nature of data projects
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Subtopic 9.3:
Using data to inform sprint planning and retrospectives
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Subtopic 9.4:
The role of the PM in a data-focused Agile team
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Subtopic 9.5:
Continuous delivery in a data-driven environment
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Subtopic 10.1:
An overview of common data science tools (e.g., Python, R)
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Subtopic 10.2:
Introduction to data visualization tools (e.g., Tableau, Power BI)
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Subtopic 10.3:
The use of cloud platforms for data projects (e.g., AWS, GCP)
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Subtopic 10.4:
Collaborative tools for data teams
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Subtopic 10.5:
Understanding the technology stack of a data project
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Subtopic 11.1:
Using data to analyze business processes
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Subtopic 11.2:
The importance of business value metrics
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Subtopic 11.3:
A/B testing and experimentation
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Subtopic 11.4:
The role of the PM in creating a data-driven culture
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Subtopic 11.5:
Translating data insights into business actions
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Subtopic 12.1:
Communicating data insights to non-technical stakeholders
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Subtopic 12.2:
Fostering a collaborative environment between PMs and data teams
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Subtopic 12.3:
Overcoming communication barriers
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Subtopic 12.4:
The importance of a shared vocabulary
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Subtopic 12.5:
Techniques for effective data-focused meetings
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Subtopic 13.1:
The ethical responsibilities of a data-focused PM
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Subtopic 13.2:
Understanding bias in data and its impact
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Subtopic 13.3:
Data privacy and security regulations
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Subtopic 13.4:
The importance of transparency and explainability in AI
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Subtopic 13.5:
Building a framework for responsible data use
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Subtopic 14.1:
Analyzing real-world data science projects
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Subtopic 14.2:
Hands-on exercises with sample datasets
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Subtopic 14.3:
Building a simple project plan for a data initiative
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Subtopic 14.4:
Presenting data-driven insights to peers
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Subtopic 14.5:
Applying course concepts to a simulated project
