Overview

All products developed for today's market are data products - running on data-derived insights to provide the right experience, to the right user, at the right time. Companies like Amazon, Netflix, Google, and more are able to provide personalized and engaging experiences to users because they utilize data science, machine learning, and artificial intelligence to better meet user needs. In the Applying Data Science to Product Management course from the upcoming Data Product Manager Nanodegree program, you will hone specialized skills in Product Management, a role with a starting base salary of $125,000. In a real-world project, you will act as a Data Product Manager for a flying taxi company called Flyber to create a data product concept, strategize the data pipeline process, and enhance the product based on user data. Be equipped to build products that leverage data to position customers and businesses to thrive.
Applying Data Science to Product Management is the first of three courses in the Data Product Manager Nanodegree program. Hone specialized skills in Data Product Management and learn how to model data, identify trends in data, and leverage those insights to develop data-backed product strategy.

Syllabus

Introduction to Data Product Management

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

Granularity, Distribution, and Modeling Data

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.

Trends, Enrichment, and Visualization

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.

Setting Product Objectives & Strategy

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.

Final Project: Develop a Data-Backed Product Proposal

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.