The Product Management for AI & Data Science Course

Position your product manager as an analytics translator who bridges technical and non-technical teams, explains AI and data science concepts, and primes stakeholders with a clear, approachable primer.

Explore how data analytics investigates questions across large data sets to improve product performance, while data science imagines innovative solutions using machine learning and algorithms.

Contrast AI with traditional algorithms by showing learning from large data sets, and highlight fields like computer vision and NLP alongside the three kinds: narrow, general, and super.

Present machine learning as a subset of AI that uses algorithm and data to build a model. Explain learning from data patterns and the need for labeling and data quality.

Explain deep learning as a subset of machine learning and its artificial neural networks. Learn to describe layers, neurons, and common structures like CNNs and RNNs to non-technical stakeholders.

As a product manager, weigh the trade-offs between deep learning and machine learning based on data availability, hardware and training time, input feature selection, and interpretability.

Compare supervised, unsupervised, and reinforcement learning and learn to choose the best method for your problem and data. See examples like loan default, clustering, and robotics.

Learn how AI and data products drive business innovations through insights, operational efficiencies, enhancements to existing products, or new offerings, illustrated by IBM, HSBC, Walmart, Salesforce, and Amazon examples.

Evaluate when to apply AI versus non-ai solutions, using a library case and user interviews to boost writers' community while noting when AI is truly needed.

Learn to apply SWOT analysis to evaluate internal strengths and weaknesses, external opportunities and threats, with data availability and competitive market analysis guiding AI-driven strategic goals.

Develop a testable hypothesis to validate AI and data product ideas, outlining the target group, problem, and expected impact. Define the goal and measurement, and design experiments before building.

Learn to test a hypothesis methodically by identifying underlying assumptions, using lean experiments, interviews, surveys, and AB tests before building a minimum viable product, and decide when AI adds value.

Apply the AI business canvas, an AI-adapted lean startup tool, to map prediction, judgment, action, and outcome with input, training, and feedback for evaluating AI-enabled business models.

Explore how an AI and data product manager sits at the intersection of AI, data, business needs, and user experience, and identify three core user types with a guiding persona.

Identify the primary user group for an AI product by uncovering their core problem with the three whys method, then prioritize the biggest pain point over isolated feature requests.

Navigate a user research funnel from market data to exploratory interviews and surveys, gathering quantitative and qualitative insights while keeping questions open-ended and avoiding bias.

Translate user research into a persona and empathy map to guide product decisions, keep the user in mind, and optimize the user experience, using Tom as a representative.

Explore how prototypes, including wireframes, AI personality design experiments, and WOZ experiments, assess user reactions and shape AI and data product experiences before building.

Develop a data growth strategy by assessing data strengths, weaknesses, opportunities, and threats, and plan data collection methods like open data, company data, crowdsourcing, new feature data, and acquisition data.

Open data provides freely available, reusable datasets for commercial or non-commercial purposes, searchable via Google's dataset search, Dataverse.org, and OpenDataKit.org.

Leverage your existing data, from books uploaded by authors to user behavior, analytics, CRM, and support emails, to train AI models with unsupervised learning and plan labeling for supervised learning.

Explore crowdsourcing labeled data for artificial intelligence, showing how data annotation drives supervised learning with human annotators and vendors while balancing cost, speed, and training needs.

Create a new feature to collect exact data for AI models, delighting users while ensuring data collection adds value beyond basic input.

Learn how acquisition or purchase of data involves cross-functional collaboration, ROI calculations, and strategic tradeoffs, including licensing, negotiation, and long-term business impact on AI products.

Centralize data from silos in a data warehouse for historical analysis using extract, transform, load; data lakes store raw, unstructured data for flexible analyses.

Discover how the AI flywheel creates a virtuous cycle of data, predictions, and user growth, making AI products continually improve and compete more effectively.

AI and data product managers solve problems by gathering core problems or core datasets, generating AI-enabled ideas, and balancing bottom-up data with EMUC sources.

Apply three ideation techniques: crazy eights, round robin, and the fake press release, to generate diverse ideas from core problems and datasets, involving cross-functional teams.

Prioritize ai and data products by mapping ideas on a prioritization graph using user benefit and technical complexity, ranking features 1–10, and balancing must-do, need-to-do, and can-do insights.

Explore how to use MVPs and the AI minimal viable data (MVD) to release early, learn from feedback, and minimize data needs while guiding AI and data product development.

Explore how data kanban adapts agile for AI and data products, balancing minimum viable data with a backlog board containing processing, modeling, training, and testing.

Evaluate whether to build AI models in-house or use enterprise AI solutions and MLaaS, guided by core business relevance, data availability, and team expertise, with NLP focus.

Discover when to use enterprise ai versus building in-house and how to evaluate vendors on data, specialization, integration, customization, security, and price.

Leverage machine learning as a service (MLaaS) to access prebuilt tools hosted in the cloud. This reduces cost, time, and infrastructure needs while enabling testing, outsourcing infrastructure, or building models.

Building in-house AI centers your core business function, enabling full data pipeline customization from data sourcing to monitoring, while outsourcing risks losing control like Target's e-commerce misstep.

Learn to monitor AI investments against model performance, identify the point of diminishing returns, productive and negative returns phases, and decide when to stop investing with input-output tracking for stakeholders.

Learn how to set realistic AI model goals using human-level, base-model, and satisficing–optimizing metrics, with practical examples from transcription and voice assistant performance.

Divide data into training, validation (development), and testing sets, typically 80/10/10. Use validation to fine-tune hyperparameters and testing to assess performance while keeping data similar to reduce bias and variance.

Explore how the confusion matrix visualizes classifier performance, detailing true positives, true negatives, false positives, and false negatives to derive precision and recall beyond simple accuracy.

Explore how accuracy, precision, recall, and F1 score measure classifier performance. Learn to read confusion matrices and compute true positives, false positives, and false negatives to select optimal models.

Compare confusion matrix metrics—precision, recall, and F1 score—to choose models based on the application: Netflix recommendations prioritize precision, hospital cancer diagnosis prioritizes recall, with F1 guiding balanced cases.

Identify and minimize the consequences of AI errors for users and business, balancing false positives and false negatives through error recovery strategies that cushion impact and preserve trust.

Explore three model deployment methods—ad-hoc SQL, batch predictions, and real-time predictions—through MLOps, weighing when each method fits internal use, frequency, or real-time needs.

Monitor deployed AI models continuously with proactive and reactive monitoring to detect data that differs from training data and potential staleness, refreshing with new training data as trends change.

Choose feedback metrics that closely correlate with model performance, using user actions as proxies. Avoid relying on a single metric; Netflix thumbs up/down illustrate both usefulness and pitfalls.

Implement user feedback loops to monitor deployed AI and data products, using qualitative and quantitative signals and humans-in-the-loop to drive improvements.

Explore shadow deployments, where a new model runs in production alongside the existing one, collecting output for analysis without user exposure and guiding safe updates.

Explore the AI hierarchy of needs from data collection and infrastructure to data analysis and data science, and learn how to align data culture, risk tolerance, and product management.

Position the AI and data team as its own business unit to align AI product life cycles and enable cross-unit collaboration. Build shared infrastructure across business units.

Explore roles in AI and data teams, including AI and data product managers, data scientists, ML data engineers, UX designers, and software engineers, emphasizing seniority and collaboration.

Coordinate AI and data teams with product managers by guiding problem identification, data sourcing, modeling, and production through a sequential workflow, ensuring gaps are managed and metrics drive success.

Adopt a triple-track agile approach—discovery, data, and development tracks—to keep validated ideas and prepared data sets flowing, enabling simultaneous work by product managers, data engineers, and data scientists.