В любой момент
3 месяца
Стоимость курса
399 USD
Подробности и регистрация

Become a Data Product Manager

Навыки, которые вы получите:
Product management Data аnаlysis

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.
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