Data Science for Product Management

Data Science for Product Management

PDMA Pittsburgh | June 7, 2021

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Why might product managers want to learn something about data science? Success in data science requires combining analytical and programming skills with subject matter knowledge. The underlying quantitative disciplines, such as statistics, mathematics and computer science require extensive study to master, to avoid drawing incorrect conclusions from limited and noisy data. Even assuming technical success, data science raises ethical dilemmas in areas such as privacy and bias. So why bother?

There are at least two reasons why product managers should care about data science. This talk will cover both.

  1. Product managers use intuition to make, or contribute to, many important decisions that should be informed by data. These include market segmentation, demand forecasting, pricing, and responding to customer feedback.  Developments in data science, combining the increasing availability of data from internal and external sources with new algorithms that exploit that data at scale, offer new possibilities for putting product management decisions on a more rigorous footing.
  2. Managers of products with a significant software component are increasingly likely to make use of data science.  Data science, and the closely related field of artificial intelligence (AI), are becoming established parts of software engineers’ toolkits. We now have products that recognize people’s faces, have natural-sounding conversations with users, and drive vehicles with limited human guidance; capabilities that would have been difficult or impossible to implement without data science and AI.  However, the new capabilities come at a cost: developing systems that learn from data requires a different mindset and approach than traditional software development.  

The main learning objectives of the talk are to enable the audience to:

  1. Identify decision points during the product life cycle where data science techniques are applicable
  2. Select techniques to analyze data for use in product management decision-making
  3. List considerations in managing data-science based products that address risks as well as opportunities.

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