Hi ,
Businesses rarely approach data scientists with well-defined problems to solve. Sometimes, the problems businesses devise aren’t
appropriate for solving using data science at all. This makes it very difficult for data science projects to succeed.
In the latest episode of Value Driven Data Science, I'm joined by Rob Deutsch, Data Science Consultant at Parity Analytic and Chief Operating Officer of AkuShaper, to discuss strategies businesses and data scientists
can employ to identify data science use cases and maximise their probability of success.
Some of the things we discuss in this episode include:
- Processes for identifying and understanding business problems, and determining whether a data science solution is appropriate and what that solution should look like.
- The different
ways in which people from different backgrounds can look at a data science problem and how that influences the questions they ask of data/the way they tackle problems.
- The role of the business vs the role of the data scientist in defining/scoping data science projects.
- How to maximise the probability of success of a data science project.
- How the data science/data analytics skill set can be transferred to areas outside of technical data analysis, such as running a
SaaS company.
You can listen to this episode by clicking the button below, or find it on Apple Podcasts, Amazon Music, Spotify or Google Podcasts.
