-Describe the marketing / data science dance - do we need data science for successful marketing? -What are the types of jobs for data scientists in marketing? -What are the key skills for a marketing data scientist? -What about other types of non-technical skills? Like domain knowledge or personal attributes? -How should data science talent vary based on Marketing context? -Describe your final piece of advice for anyone getting into the marketing data science space?

• What is this
• How this interacts with other C”suite
• Technical business culture
• What can this solve for us

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Hua Ai:

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• What are the types of data science work able to get automated, managed with AI?
• How can AI help, what steps are reduced 
• How does this change the necessary skill set?
• Are we writing ourselves out of a job?
• Final piece of advice for leveraging AI to reduce data science barriers 

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Giang Do:

• What is a Data Science career?
• Value of Experience, Exposure and Knowledge.
• What are the tricks to a successful career?
• Tips you wish you had known.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Deepac Jose:

• what are some of the capabilities
• importance of challenging status quo
• breaking silos
• how to know you’ve won
• final piece of advice

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Ned Caron:

• What do we mean by data-informed?
• How does this differ from data-driven or other philosophies?
• What are the challenges and benefits for adopting data-informed decision making?
• What advice would you offer to someone trying to move in the data-informed direction?

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Jorge Figueroa:

AI Monopoly - Join or fight. Some developers are scared about AI being monopolized instead of thinking about the possibilities and opportunities new AI platforms represent in their projects.

AI - A new danger to some professions or a new tool?  
Will use reporting as an example. Reports were created by IT specialists and were fixed and timely to create. These were mostly replaced by BI tools. Surely there were report-experts how thought BI was a danger to their work at some point in time that later migrated to using BI tools and found them a great evolution from basic reporting. Even when integrating ML or Data Science projects is faster and easier with newly available platforms and resources, experts are still needed to work out these integrations. You should not consider your expertise replaced but empowered by these new tools. Millions of research hours and dollars in investment are now at your disposal to expedite your work. I recently had a talk with some friends from the art community and they feel threatened by tools like Dall-e, Stable Diffusion, Craiyon, etc. I explained new tools often look threatening to some professions but are an opportunity. Those who learn to use them faster and better have an advantage over others. As an example, Prompt Engineering is now an important competence.

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Dinakar Deshmukh:

• what does it mean for a solution to be scalable
• how have we lacked in this area as an industry 
• why it’s important to make solutions that last
• the future of data science products and scalability

In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Chad Harness:

• describe automl to the business person
• how do these types of tools work
• what are the right ways to leverage automl
• expectations for the future of automl tools

In this episode of TAG Data Talk, host Dr. Beverly Wright talks with Justin Grieshop:

• Describe the backgrounds and disciplines in data science and analytics
• How to show the importance of diversity of thought
• Advice for understanding the right education and how to extract value from those resources

In this episode of TAG Data Talk, we talk with Patrick Halpin about Building and Managing the Data Science Product Development Process