TAG Data Talk TAG Data Science & Analytics
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- Tecnologia
This is TAG Data Talk where we discuss news, trends, and ideas in the world of Data Science and Analytics. TAG Data Talk is hosted by the Technology Association of Georgia's Data Science and Analytics society.
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Getting the Absolute Best Data Science Talent to Solve Marketing Problems
-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? -
The Role of the CAIO
What is this
How this interacts with other C”suite
Technical business culture
What can this solve for us -
Reducing Barriers to Complex Data Science Entry by Leveraging AI
In this episode of TAG Data Talk, Dr. Beverly Wright discusses with Hua Ai:
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 -
Defining and Adapting your Data Science Career
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. -
Building Data Science and AI Capabilities to Last
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 -
Applying a Data-Informed Approach to Influence Leaders and Business Decision-Makers
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?