Leverage data to build products that deliver the right experiences, to the right users, at the right time. Lead the development of data-driven products that position businesses to win in their market.

Necessary preparation

• No prior experience with data modeling & data engineering is required. However, a basic understanding of data terminology (i.e. big data, database, algorithms, etc.), some experience with data analysis (basic SQL & Tableau), and a general understanding of product management is helpful.

For those who want to

• Learn how to apply data science techniques, data engineering processes, and market experimentation tests to deliver customized product experiences.
• Develop data pipelines and warehousing strategies that prepare data collected from a product for robust analysis. 
• Learn techniques for evaluating the data from live products, including how to design and execute various A/B and multivariate tests to shape the next iteration of a product.

The Program 

1. Applying Data Science to Product Management.
2. PROJECT — Develop a Data-Backed Product Proposal.
3. Establishing Data Infrastructure.
4. PROJECT — Build a Scalable Data Strategy.
5. Leveraging Data in Iterative Product Design.
6. PROJECT — Create an Iterative Design Path.

What will you learn

•  The role of data product managers within organizations and how they utilize data science, machine learning, and artificial intelligence to solve problems.
• How to visualize your data with Tableau for statistical analysis and identify unique relationships between variables via hypothesis testing and modeling.
• Data infrastructure components including data pipelines, data producers, data consumers, data storage, and data processing.
• The nuances of evaluating strategic decisions for data pipeline technology, including security and compliance.
• Solutions for real-world data infrastructure problems and evaluate tradeoffs.
• To understand which data is best collected through quantitative versus qualitative methods, and how to interpret it.
• How to apply chi-square tests to determine if results from data analysis are statistically significant and etc.