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.
- 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.
- Applying Data Science to Product Management.
- PROJECT — Develop a Data-Backed Product Proposal.
- Establishing Data Infrastructure.
- PROJECT — Build a Scalable Data Strategy.
- Leveraging Data in Iterative Product Design.
- PROJECT — Create an Iterative Design Path.
What will you learn
- Understand 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.
- About data infrastructure components including data pipelines, data producers, data consumers, data storage, and data processing.
- Master the nuances of evaluating strategic decisions for data pipeline technology, including security and compliance.
- Create solutions for real-world data infrastructure problems and evaluate tradeoffs.
- 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.