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, and Google 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 Establishing Data Infrastructure 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 Flyber, a flying-taxi service that has been massively successful in New York City after its first product launch, to create a data strategy to handle the massive amount of incoming data and process it to derive the insights needed to grow the business.
Establishing Data Infrastructure is the second of three courses in the Data Product Manager Nanodegree program. Hone specialized skills in Data Product Management and master the nuances of evaluating strategic decisions for data pipeline technology and create solutions for real-world data infrastructure problems.
Overview
Syllabus
- Introduction to Data Pipelines
- Begin by understanding the importance and need of data pipelines and the various components of data pipelines, and learn how to organize data pipeline components to automate end-to-end data flow. Then, create conceptual data pipelines and conceptualize classic data problems that can be addressed by data pipelines.
- Data Consumers
- Learn about primary data consumers, their data needs, and how to identify data consumers in an organization and their relevant data use cases.
Develop an understanding of the components of a relational data model and apply relational data models to business scenarios. - Data Producers
- Learn how to create event data models and implement them to get business insights, and use data collected from event models to calculate product KPIs. Identify primary data producers in an organization and distinguish between backend data producers (SaaS, ERPs, and data stores) while also differentiating between types of data (structured vs. semi-structured vs. unstructured).
- Data Strategy
- Understand the difference between ETL and ELT processes, distinguish between batch processing and stream processing, and learn to select the appropriate data processing components for a product based on data needs. Differentiate between a data warehouse and data lake, and between SQL and NoSQL databases, and determine the appropriate data storage components for a particular data infrastructure of a product based on data needs. Assess capabilities of various data warehousing options (build vs buy, cloud vs on-prem, open source vs proprietary, and insource vs outsource) to make strategic decisions for data infrastructure, and evaluate data security and compliance product use cases (PII, PCI, HIPAA, GDPR, and CCPA).
- Final Project: Build a Scalable Data Strategy for Flyber
- In this project, you will act as a data product manager for Flyber, a
fictional flying-taxi service, and create a data strategy to not
only handle the massive amount of incoming data, but also process
it to gain business insights. First, you will define the data needs of primary business stakeholders within the organization and create a data model to ensure the data collected supports those needs. Then, you will perform the necessary extraction and transformation of the data to make the data relevant to answer business questions. Finally, you will interpret data
visualizations to understand the scale of Flyber’s data growth and choose an appropriate data warehouse to enable that growth.
Taught by
Vaishali Agarwal
