Enrollments for individual courses are no longer available for this program. Visit our Data Product Manager page to learn more about enrolling in the complete Nanodegree program.
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Prior Data Analysis & Product Management Experience Recommended
Explain the concept and history of data product management, and be able to distinguish the different types of data product managers. Then, identify the various internal stakeholders that data product managers work with. You will understand the fundamentals of general product management from talking to customers, analyzing data, designing high-level solutions, prioritizing work, setting a roadmap, facilitating development, launch communications, and product iteration
Analyze what is being measured in a dataset and explain the benefits of aggregates or roll-up tables. Then, compare and contrast the differences between fact & dimensional tables, and calculate and analyze the distribution of a dataset.
Identify and differentiate different visualizations, and justify when to apply the right visualization for the appropriate analyses (spatial, temporal, distribution, correlation) - box plot, line chart, donut chart, density map, histogram. Then, implement enriching datasets, and utilize common online repositories for publicly available datasets for analysis.
Interpret data and insights to come up with product objectives. Design KPIs that measure if your products are meeting their objectives and utilize best practices and different techniques for setting up explicit feedback mechanisms. Then, create experiments that generate meaningful results in a timely, resourceful manner and drive instrumentation strategies for proper event data collection.
A key responsibility of data product managers is analyzing market data to propose new product opportunities. In this project, you will apply the skills acquired in this course to create the MVP launch strategy for the first flying car taxi service, Flyber, in one of the most congested cities in America -- New York City. Your team acquired taxi data for a comparable initial analysis. The dataset contains real taxi drop-offs and pick-ups in New York City. First, you will analyze the existing use cases for and identify temporal, behavioral, and spatial trends of ground-based taxis from the dataset. Next, you will deep-dive into user research data, to understand the general sentiment, desire, concerns, and use cases of a flying cab service to prospective customers. Finally, you will synthesize your insights to create a data-backed product proposal that recommends what features the first flying taxi service should have to maximize consumer delight, adoption and profits.