AI Data

The AI+ Data certification equips professionals with vital skills for data science. It covers key concepts like Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced topics such as Generative AI and Machine Learning, preparing them for complex data challenges. The program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. Personalized mentorship, immersive projects, and cutting-edge resources ensure a transformative learning journey, preparing individuals for success in AI and data science.

Data-Driven Delivery: Why AI+ Data™ Mastery is a PM’s Technical Powerhouse

In the world of project management, “Garbage In, Garbage Out” is a project-killing reality. Whether you are managing the Quality theme in PRINCE2 or refining a Product Backlog in Scrum, your project is only as strong as the data supporting it.

The AI+ Data™ specialization transforms you from a PM who “coordinates tasks” into a technical strategist who can oversee the entire data lifecycle—a critical skill for any PM leading digital transformation or high-stakes AI initiatives.

1. Hardening the “Quality” Theme (PRINCE2® Perspective)

In PRINCE2, the Quality Register ensures that deliverables meet the customer’s expectations. In AI projects, “Quality” is synonymous with “Data Integrity.”

• The Data Advantage: You will gain the technical literacy to audit data pipelines. You’ll be the PM who can tell the Project Board exactly why a model isn’t performing, identifying issues in data collection or cleaning before they cause a “Stage Exception.” You don’t just manage the timeline; you manage the truth behind the project.

2. Eliminating Technical Debt (Scrum Perspective)

For Scrum Masters, the most common “Blocker” in AI sprints is poor data availability or messy datasets.

• The Data Advantage: By understanding the AI Data lifecycle, you can help the Product Owner prioritize “Data Debt” in the backlog. You’ll be able to facilitate better communication between Data Engineers and Scientists, ensuring that the “Definition of Done” includes rigorous data validation, leading to more stable increments and higher velocity.

3. Precision in Estimation and Forecasting

Most AI projects fail because PMs underestimate the “Data Preparation” phase.

• The Candidate Edge: This certification proves you understand the complexities of data labeling, feature engineering, and storage. When you build a project plan in [Microsoft Project] or [Smartsheet], your estimates will be rooted in technical reality, drastically reducing the risk of late-stage “scope creep” caused by unforeseen data hurdles.

4. Bridging the Gap Between Engineering and the C-Suite

As a PM, you are the translator.

• The Candidate Edge: You will have the skills to explain complex data concepts—like data bias, leakage, or synthetic data—to non-technical stakeholders. By showing that you can manage the data assets of a project using tools like [Power BI], [Tableau], or [Snowflake], you position yourself as a leader who can be trusted with the organization’s most valuable intellectual property.

The Technical Edge: In an AI-first world, “Data is the New Oil,” and the Project Manager is the refinery manager. This certification proves to employers that you have the technical depth to lead high-complexity data projects with the structured discipline of a professional PM. You don’t just lead the team; you understand the engine.

You will learn

• Core Concepts Covered: Data Science foundations, Python, Statistics, and Data Wrangling
• Advanced Topics: Dive into Generative AI, Machine Learning, and Predictive Analytic
• Capstone Application: Solve real-world problems like employee attrition with AI
• Career Readiness: Develop skills for AI-driven data science roles with hands-on mentorship

Skills you’ll gain

Prerequisites

• Domain Awareness: Basic knowledge of computer science and statistics.
• Curiosity about Data Analysis: Keen interest in the tools and processes of data
  analysis.
• Learner Mindset: Willingness to learn programming languages such as Python
  and R.

Tools you will explore

• Google Colab
• MLflow
• Alteryx
• KNIME